Neural_Net_Training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RobertoAlessandri/CNN_DOA/blob/main/Neural_Net_Training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Neural Networks "
      ],
      "metadata": {
        "id": "6ErvgA7v4o1L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is based on the paper *Deep learning assisted sound source localization using two orthogonal first-order differential microphone arrays* [Nian Liu, Huawei Chen, Kunkun Songgong, et al]. Features and networks' architectures are a direct implementation of the methods discussed in that paper, refer to it for a detailed explanation."
      ],
      "metadata": {
        "id": "SQLnNNrM40SS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is subdivided into 4 sections:\n",
        "\n",
        "1.   **Imports and auxiliary functions** where we import libraries and define the core functions for feature extraction\n",
        "2.   **Feature extraction and data exploration** where we plot and play some simulated examples together with the SI features\n",
        "3.   **Training** where we show the learning curves\n",
        "4.   **Testing** where we evaluated the models' performances\n",
        "\n"
      ],
      "metadata": {
        "id": "PKIg7LqO45bY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PLEASE NOTE**: The actual training of the neural networks has been performed on a local machine with a GPU. In this Colab we uploaded the training output for easy consultation of the loss and accuracy curves."
      ],
      "metadata": {
        "id": "wPuGa72h7q-r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Imports and auxiliary functions"
      ],
      "metadata": {
        "id": "v4z5w5Fl4jph"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CrPRRG1Mu6Uz",
        "outputId": "33697546-510f-4441-9d51-0a3c5ac6da59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "# Imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa, librosa.display\n",
        "import IPython.display as ipd\n",
        "from pathlib import Path\n",
        "from tqdm import tqdm # Progress bar\n",
        "from sklearn.utils import shuffle\n",
        "import random\n",
        "import sklearn\n",
        "import re \n",
        "plt.style.use('seaborn')\n",
        "\n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting Google Drive to retrieve the datasets\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svhf53nXD6bL",
        "outputId": "704d9269-d5bb-4386-ff7a-69b48e43edc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lGfXepRbu6U4"
      },
      "outputs": [],
      "source": [
        "# Generate a matrix containing audio data as columns\n",
        "# This function assumes the alphanumerical ordering of the filepaths\n",
        "def extractAudiodata(filepaths, sample_len):\n",
        "  sentencesNum = int(len(filepaths)/4) # Number of sentences\n",
        "  sentencesId = np.zeros([sentencesNum, 1]) # Array containing all the sentences' ID\n",
        "  labels30 = np.zeros(sentencesId.shape) # Lables for the model with resolution 30°\n",
        "  labels10 = np.zeros(sentencesId.shape) # Lables for the model with resolution 10°\n",
        "\n",
        "  # Ouput audio matrix N*M*L\n",
        "  # N : Length of the audio files in number of samples\n",
        "  # M : Number of microphones\n",
        "  # L : Number of sentences\n",
        "  audioSignals = np.zeros([int(sample_len), 4, sentencesNum]) \n",
        "  \n",
        "\n",
        "  j = 0 # j is indexing sentences, range: [0, sentencesNum - 1]\n",
        "  for i in tqdm(range(0, len(filepaths), 4)): # i is indexing samples, range: [0, len(filepath)] with step 4\n",
        "    # Selecting the four microphones' input for a given sentence\n",
        "    micsABCD = filepaths[i:i+4]\n",
        " \n",
        "    for k in range(0, len(micsABCD)): # k is indexing mics, range: [0,3]\n",
        "      # Populating the ouptut matrix\n",
        "      # Loading k-th microphone's input of the j-th sentence as a column vector\n",
        "      audioSignals[:, k, j] = librosa.load(micsABCD[k], sr=None)[0]\n",
        "\n",
        "    # Extracting labels and sentence id from the sample relative to the first microphone\n",
        "    fileid = micsABCD[0].split('.wav')[-2]\n",
        "    sentencesId[j] = fileid.split('-')[-3]\n",
        "    labels30[j] = fileid.split('-')[-2]\n",
        "    labels10[j] = fileid.split('-')[-1]\n",
        "\n",
        "    # Increasing sentence's index\n",
        "    j += 1\n",
        "\n",
        "\n",
        "  return audioSignals, sentencesId, labels30, labels10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sn88FCrHu6U6"
      },
      "outputs": [],
      "source": [
        "# This function sorts alphanumerically the list l\n",
        "def sorted_nicely(l):\n",
        "    convert = lambda text: int(text) if text.isdigit() else text\n",
        "    alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n",
        "    \n",
        "    return sorted(l, key = alphanum_key)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "btf0uSSMu6VA"
      },
      "outputs": [],
      "source": [
        "# Sound Intensity (SI) estimation \n",
        "def computeSI(X1, X2, X3, freqsMatrix, isOblique=False, X4=0,  d=0.04, rho=1.225, beta=1e6):\n",
        "  denominator = (freqsMatrix)*d*rho\n",
        "\n",
        "  if isOblique:\n",
        "    # Paricle velocity estimation in TF-domain relative to subarrays' directions (pi/4, 3/4pi, -pi/4, -3/4pi)\n",
        "    X0 = (X1 + X2 + X3) / 3\n",
        "    V1 = (np.sqrt(2)*1j*(X2 - X1)) / denominator\n",
        "    V2 = (np.sqrt(2)*1j*(X2 - X3)) / denominator\n",
        "  else:\n",
        "    # Paricle velocity estimation in TF-domain relative to principal directions (x and y)\n",
        "    X0 = (X1 + X2 + X3 + X4) / 4\n",
        "    V1 = (1j*(X3 - X1)) / denominator\n",
        "    V2 = (1j*(X4 - X2)) / denominator\n",
        "\n",
        "  # Witheningh weight\n",
        "  W = (np.abs(X0)**2 + beta*(np.abs(V1)**2 + np.abs(V2)**2) )**0.5 + np.finfo(np.float32).eps\n",
        "\n",
        "  # Extracted SI features\n",
        "  I1 = (np.real(X0*np.conj(V1)) / W).T\n",
        "  I2 = (np.real(X0*np.conj(V2)) / W).T\n",
        "\n",
        "  # Grouping features into a matrix\n",
        "  matrix = np.zeros([I1.shape[0], I1.shape[1], 2])\n",
        "  matrix[:, :, 0] = I1\n",
        "  matrix[:, :, 1] = I2\n",
        "\n",
        "  return matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGdcP9wIu6VA"
      },
      "outputs": [],
      "source": [
        "# Compute the SI features for each designated DMA (Differential Microphone Array): principal directions + subarrays\n",
        "def featureExtractor2(micSignals, d=0.04, rho=1.225, NFFT=1024, sr=16000, beta=1e6):\n",
        "  # STFT variables\n",
        "  frame_length = NFFT\n",
        "  hop_length = int(frame_length/2) # 50% overlap\n",
        "  num_samples = micSignals[:,0].shape[0] # Length in samples\n",
        "  num_frames = int(1 + np.floor((num_samples - frame_length) / hop_length)) # Resulting number of frames\n",
        "\n",
        "  # freqMatrix is a matrix that contains the angular frequency axis repeated on each column\n",
        "  # It is used for the SI estimation \n",
        "  freqs = librosa.fft_frequencies(sr=sr, n_fft=NFFT) / (sr/2) * np.pi + np.finfo(np.float32).eps\n",
        "  freqs = np.reshape(freqs, (len(freqs),1))\n",
        "  freqsMatrix = np.tile(freqs, (1, num_frames))\n",
        "\n",
        "  # Init output matrix\n",
        "  gamma = np.zeros([num_frames, int(NFFT/2 - 1), 10])\n",
        "\n",
        "  # STFT microphones' signals\n",
        "  P1 = librosa.stft(micSignals[:,0], n_fft = NFFT, hop_length = hop_length, win_length = frame_length, window='hann', center=False)\n",
        "  P2 = librosa.stft(micSignals[:,1], n_fft = NFFT, hop_length = hop_length, win_length = frame_length, window='hann', center=False)\n",
        "  P3 = librosa.stft(micSignals[:,2], n_fft = NFFT, hop_length = hop_length, win_length = frame_length, window='hann', center=False)\n",
        "  P4 = librosa.stft(micSignals[:,3], n_fft = NFFT, hop_length = hop_length, win_length = frame_length, window='hann', center=False)\n",
        "\n",
        "  # Principal directions SI extraction \n",
        "  # First and last frequency bins are excluded because always equal to 0\n",
        "  gamma[:,:,0:2] = computeSI(P1, P2, P3, freqsMatrix, isOblique=False, X4=P4, d=d, rho=rho, beta=beta)[:,1:int(NFFT/2),0:2] \n",
        "  \n",
        "  # Subarrays SI extraction\n",
        "  gamma[:,:,2:4]  = computeSI(P4, P1, P2, freqsMatrix, isOblique=True, d=d, rho=rho, beta=beta)[:,1:int(NFFT/2),0:2]\n",
        "  gamma[:,:,4:6]  = computeSI(P1, P2, P3, freqsMatrix, isOblique=True, d=d, rho=rho, beta=beta)[:,1:int(NFFT/2),0:2]\n",
        "  gamma[:,:,6:8]  = computeSI(P2, P3, P4, freqsMatrix, isOblique=True, d=d, rho=rho, beta=beta)[:,1:int(NFFT/2),0:2]\n",
        "  gamma[:,:,8:10] = computeSI(P3, P4, P1, freqsMatrix, isOblique=True, d=d, rho=rho, beta=beta)[:,1:int(NFFT/2),0:2]\n",
        "\n",
        "  # Gamma is a 14*511*10 matrix where\n",
        "  #   - 14 is the number of frames\n",
        "  #   - 511 is the number of frequency bins\n",
        "  #   - 10 is the number of channels (SI for each DMA) \n",
        "\n",
        "  return gamma"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Feature extraction and data exploration"
      ],
      "metadata": {
        "id": "rvQZtoC8BpHK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The wav files in each dataset are name encoded. \n",
        "\n",
        "**Name structure:** SA{ID}-{MIC}-{SENTENCE ID}-{CLASS30}-{CLASS10}.wav\n",
        "\n",
        "The 'SENTENCE ID' identifies the unique sentence from the TIMIT dataset. But since we use 4 microphones in our simulation, each sentence is recorded 4 times. Thus the 'MIC' label identifies each individual microphone with the four letters A, B, C, or D to indicate the first, second, third, and fourth microphone respectively. The label 'ID' is used as a general unique identifier. Finally, 'CLASS30' and 'CLASS10' are the labels for the two neural networks, the first that classifies with a resolution of 30° and the second with the resolution of 10°.\n",
        "\n",
        "\n",
        "E.g. SA17-B-0-8-23.wav: \n",
        " * ID: 17 \\\n",
        " * MIC: B (second mic) \\\n",
        " * SENTENCE NUM: 0 \\\n",
        " * CLASS30: 8 \\\n",
        " * CLASS10: 23 \\"
      ],
      "metadata": {
        "id": "D3IuWAZ1BqPb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y95wcp18u6U5",
        "outputId": "dc3affa6-c8a1-47b7-83a4-369db446f474",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1-B6wbQJimyIAQNh3Ccp578yl6uudDF9j/CNN_DOA/Dataset/MATLAB_TEST10\n"
          ]
        }
      ],
      "source": [
        "# Move into folder containing the test set\n",
        "%cd /content/drive/MyDrive/Github/CNN_DOA/Dataset/MATLAB_TEST10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3GoEMu0Yu6U6"
      },
      "outputs": [],
      "source": [
        "# Get list of audio files in the current folder and sort them\n",
        "audiofiles = [str(file) for file in Path().glob('SA*.wav')]\n",
        "audiofiles = sorted_nicely(audiofiles)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing random files' names as example\n",
        "random.choices(audiofiles, k=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gV8Sas41GrWg",
        "outputId": "e87d5fd9-f253-4888-a552-5993544d1316"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SA-830-C-207-20-20.wav',\n",
              " 'SA-1418-C-354-35-35.wav',\n",
              " 'SA-359-D-89-8-8.wav',\n",
              " 'SA-1048-A-262-26-26.wav',\n",
              " 'SA-219-D-54-5-5.wav',\n",
              " 'SA-1296-A-324-32-32.wav',\n",
              " 'SA-1387-D-346-34-34.wav',\n",
              " 'SA-1409-B-352-35-35.wav',\n",
              " 'SA-50-C-12-1-1.wav',\n",
              " 'SA-856-A-214-21-21.wav']"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NSNpjQb2u6U7",
        "outputId": "4dc76016-ac0a-4ca2-ed37-97a5ed8e5ffe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 360/360 [00:29<00:00, 12.16it/s] \n"
          ]
        }
      ],
      "source": [
        "# Exctracting audio for the duration of 500 ms with respective labels\n",
        "sr = 16000\n",
        "duration = 0.5\n",
        "\n",
        "audioMatrix, sourceIDs, labels30, labels10 = extractAudiodata(audiofiles, sr*duration) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uf00JIuou6U9",
        "outputId": "3f80f3d7-bd73-493f-bea8-90117163e22a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "audioMatrix has shape (8000, 4, 360) where 8000 is length in samples, 4 are the microphones and 360 are the number of setences\n"
          ]
        }
      ],
      "source": [
        "# Checking shapes\n",
        "print('audioMatrix has shape {} where {} is length in samples, {} are the microphones and {} are the number of setences'.format(audioMatrix.shape, audioMatrix.shape[0], audioMatrix.shape[1], audioMatrix.shape[2]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bbfVJVOYu6U9",
        "outputId": "a728c8f4-d741-4f28-fdd6-0127b3b5ede5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "labels10 is an array of length 360, one for each sentence\n",
            "\n",
            "All the possible classes are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
            " 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.]\n"
          ]
        }
      ],
      "source": [
        "print('labels10 is an array of length {}, one for each sentence\\n'.format(labels10.shape[0]))\n",
        "print('All the possible classes are: {}'.format(np.unique(labels10)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qCjCR3IIu6U_",
        "outputId": "1834632c-55aa-4547-c8d2-256e60bec47d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
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            "text/plain": [
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            ],
            "text/html": [
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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              "                    Your browser does not support the audio element.\n",
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              "              "
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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EHD6LeQAACELT+907a4dMdc+I7r2/AyZ62sHAACV+PgZnd7wM9/0KfFrB9cOdO0AAAAA2gH8DFLiFTK69gjmqirj50v8awcdGHfgdO1YSFLiCOZjIR0YF7JnFIkfLeTj56JE683TG1Li1w7rzcs1Ob0+I9/09CZAo6JEJv534N/08DPuwB0YAAC69nD6/Ay+6Sb+/Ax07fQmLeSFLH/GyzWV+C3k1w5w+rr2AAAhC7UDuvaxECnxQhb8DCb+EMymNynxvumxELUDJv6QBdoBLeQSP167gTnj51w7+pnETyb+d+CMErEQAACQBSnx1w4I5rEQQhZ/xokfvuljIRS/QhZnFH/G/AwE82cU7sDTGy3klfgp8bEQ682qKsnCS/yuHVLimet34Ms1oNFS4vgZ/AykxIwSRgkp8U/vYyFw+lbVqiqQBS3kwtwvV/6MEj8I5kYJKfH4Gb7pDNnpTXvTuvaQBaoq681L/Bkl59rf9IUsvunC3PQm1w5W1YwSHRjC3Hfg8DNP7y3kZxQdGMLc4+cVMoO5PiMp8QAA4+cdGL7psRC+6YwSoNE+I2sHcPqd3qoq2gFW1QTzpjeg0fwMlfjXDnTt/Az8DDm9FTJP70/vdO2uHQjmIQu+6Snx+BkAAMLc+BkhC0v84+euHXTtFTIUv8RPrKoOTGKuxE/Jws8oCObTG5nrS/yJH+vNJv61A7EQwtyuHWsHUuIm/vAzd+AE84wSRgkM2doBOjBeu2MhcPop8WsHBPNrB1LiqipW1br2PiOZ63D6kAVrBzXKGSVrB5nrVtUKWcbPBPNnFNcOKfH8DMLcYyFw+rUDnd4+IzXK0xu+6dMbdO0dGHfgPiN07U/vqiraAcbPRgmxECb+nd43PXTtmev0JuPnjBIt5IE5rKrPKHD6uvad3hUy682+6RkljBLuwBkljBJS4owS2gG1A77pgTnRqPQmS/xnFFrIYC5w+kYJ3/RP77EQ3/S1AzHX0xvTG5nrdO1nFK4dVtVw+vwMtQMm/rr2Rgm1Ax0Ynd4AALr2AABS4q4d1w4QzGMhS/wx1yb+kAUI5inx/Azf9K4d/AxS4inx0xsAADm9HRimN+vNmeumN+PndO0+Iy3k59qJHyb+d+BL/PgZDNlL/GcUpMQ+I4Us1ZtjIR0Y0xvys6Y3VtX8DHD6IQt34IwSsRB/xtMbcPpnFFLiqipW1aoqlfjaATHXIQtrBynxuvaxEFbVlfhCFtoBMdc+I0YJBPO69mcUjBKZ65X4tQMdGAzZ1w669t/0kAUI5kYJRgmV+DHXzyhGCQTzDNk+Ix0YMdfGz/QmjBLNtQTztQO1A3/GjBIdGAzZCOY+I0YJvukE89cOjBIt5HTtZxSxEATz59oZJePnUuL8DPwMS/y69mcUMddGCd/0rh2d3k/v/AzTG9oBdO2QBc8oBPPn2h0Y0xsE8y3kIQuV+AAAtQMAAHvTqiox17UDwtzPKAAA/Ay+6bEQcPpnFJX4Jv78DN/0BPOV+GcUvumxEJAFlfgm/kv8dO2V+GsHT+9rB0IWlfhL/N/0BPPj5z4jT+8x19cOZxSZ66DR/AzTG5AFUuKMEnD6+BmQBX/GtQPC3N/0metjITm91w6+6dMbf8bTG0YJd+D8DCb+uva69kIW2gG69k/vkAXXDinxAABrB64d3/QE80YJFTKd3ufaS/zTGynxAAAm/pAFiR/C3ATz+BmJHy3kUuIdGGcU3/Sd3hklCOYt5GcUT+9P74wSZxTf9Cb+IQsm/msHBPME8/gZS/xS4vQmmesp8cLcqiqV+C3kPiNCFgjmkAXf9L7pYyEE80/vCOb0JvgZqLeQBWAuUuJayEIWAADf9EIWAADuwEYJJv4t5NcOAAC69rEQ1w5w+inxrh2Z6y3ktQNnFLr2tQNS4kYJtQO1A+faS/xgLgTzLeQAADowKfHC3HD6/Ay1A77pJv4p8QAAIQu1AwTzRgkhC5X4KfEm/k/vtQMhCwjmcPqxEHTtT+/TG1LicPraAfwMnd6QBSb+tQNGCSnxuvaQBa4dLeS69msHrh2Z6+fa+Bn4GSb+vulGCUYJvumV+JX4BPPaAdoBHRh07UYJ2gG69pX41w7f9EYJCOaJH9/0YyHn2rEQAAAp8XTtS/xnFHTtBPMp8YwS3/S1A9/0wtzTG7EQdO3aAbEQvumV+B0YtQO69msHJv7XDgTzlfjC3Bkl/AzC3JnrGSVL/GcUNcom/gTzHRh70yb+cPrXDnD6AADf9NcO3/Rw+sLc2gFGCUYJ3/SZ69MbZxRS4pX4RgmZ65AFtQNL/Cb+Rgm69nD64+chCwjm2gFrB7UDAAC1A/wM1w78DDHXIQv8DL7p2gG1A/wM3/Rw+rr2Jv6QBZX4lfjaAcLckAVrB5AFdO34GU/vkAWQBXD6/AwdGJAFLeSV+PwMKfHaAQAAdO1w+rEQT++1A3TtkAWQBQAA4+dGCUYJRglP79/0kAUAANoBlfgI5k/v+BlS4gzZHRjXDnD6BPMm/gTzrh0E877p2gGqKgTzvukAACELmesm/r7pKfG1A0v8cPq69gAAcPrf9Cb+Jv669pX4kAXf9AAARgnf9Ev8dO0hC7r2+BmV+AAAlfhrB0YJAAAI5vwMRgnf9OPnIQv8DC3kuvYm/msHtQOV+Lr2/AxrB7r2AAD8DB0YvulP79oB2gHf9GsHdO21A0IWQhZP75AFawe+6cLccPoE89oBkAWV+HD61w5rB9/0awchC77p/AwhC9/0jBIm/iELmevXDt/0sRAp8QAAdO1w+kv8awcE8wAAlfiMEk/vtQPaAbr22gEhCwTz+BlrB3D6metrB3TtS/z8DCnxuvZL/GsH3/RP70YJmetrBynxuvaQBQAAcPprB0v8lfj8DGsHmevf9NcOS/wm/nTt2gGV+JAFvulL/EYJlfgE87UDIQsp8QTzAAAE85nrawcm/kv8BPPaAWsHmevaAZX4Jv6V+AAA3/QAALEQlfhL/PwMAABL/NoB2gEp8ZAFsRC+6Uv8kAVw+k/vS/xw+iELawe1A4wSawcAALr2ZxQAAEv8AADaAZ3etQMm/iELKfFGCd/0lfiV+GsHlfgp8SELawcm/iELtQO69rr2meshC9oBS/wp8QAAIQsE8ynx1w61A0v8awe69t/0aweQBU/vuvYhC9/0awfaAZAFlfhGCSb+dO0AAEv8kAVL/NoB/AwE87UDcPq69nD6AAC69ib+BPMAALr2tQO69nD6S/wAAJAFKfFGCb7pcPom/kv8BPOxEHD6tQNw+rUDKfEAALUDtQPaASb+S/wm/nD6Jv6QBbUD3/SQBdoB2gFw+ib+kAUAAAAA3/QAAAAAuvYp8Sb+tQPaAQAAAABrB3D6kAUE83D6AAAhC77plfj8DLEQBPPf9GsHkAXf9N/0Jv4AANoBcPqQBSb+S/zaAdcOLeTaAWsHawff9LUDJv6V+CnxJv4p8XD6S/y1A7r23/QE8wAAuva+6Snx3/Qm/inxcPpGCZAFJv61A0YJ2gFL/HD6AAC1A9/0BPNw+toBlfgE85AFawe69kYJkAWV+AAAsRAm/nD6IQtGCWsHRglGCWsHAACxELEQawf8DK4d0xuQBbEQ1w5nFCELAAAE85AFS/wt5ATzBPOd3gTzT+907Znr1w5L/E/vlfj8DLr2AAAE89oBkAUm/rr2AADTG0v8RgnTG7EQBPOxELUDmeu+6Sb+dO0E89/0S/zj53D6BPMp8U/vS/y69gTzBPMAACb+S/zf9Lr2S/zf9AAAAAC69pX4cPoAAATzdO0p8Sb+vum69pnr3/RL/Cb+uva1A7r2kAWQBQAAkAXaAWsHlfjaAUYJlfgp8XD63/RS4p3eLeQE8wzZKfG69kv8kAVw+mcUIQv0JhklhSyqKn1GpjfLNcs1pjeqKkIWZxRrB7UDlfgE8ynxd+Dj5+faLeTC3L7pUuJS4nfg3/R07br2T+9L/LUDS/zXDkIWRgmxEB0Y+BkhC/wM/AyxEAAAcPoAAHD6T+9S4pnr4+fn2uPnUuLn2pnr59q+6b7p3/Qt5ATzJv7f9HTt/AzaAdoBIQtjIUYJZxSJH2cUawfXDkv8KfGV+FbV4+ct5OPnUuKxECb+kAXTG2MhjBLTG8s1HRj4GUIWiR+QBbUDAADf9JX43/QI5i3kmest5C3kwtzn2gjmd+AI5ufaCObf9ATzvumV+JAFAAC69msHcPpL/N/0T+8t5C3kMdcx1+PnCOYE87r2ZxTXDoUspjcOTMdCL1fpTVhINz0SP9Mb+BnaAUv8wtwt5FLiMdcM2S3kwtx07QTz3/SZ60v8mesp8XTtuvbf9HD6cPom/owSrh2uHfgZFTKFLD4jPiP4GUIWawe1A0/v3/Qt5OPnoNF34Ofand4x153eUuIt5C3kUuII5pnrT++Z65X4Jv6QBYwSZxRCFmcUzyhnFNcOZxRrB5X4BPN07efad+B34MLcCOa69toBjBL4GfQmNz2iRFw7fUbsQDowhSxjIQAAkAWV+MLc59pW1cbPNcoQzH/GEMx703vTVtWd3nfg59pS4pnrKfFP7/wMawfXDrEQ0xuMEkYJlfiV+HTtLeTn2qDRe9Pn2jHXvulP7yELHRjLNYE5nlGeUZteVFVYSDc9OjCuHdoB3/RS4p3eMdeg0VbVDNkt5OPnJv669toBcPoAACb+cPqV+Ev8Jv5GCSb+1w6xED4jYyE6MIUsFTI6MBklQhYhC0v8UuIM2RDMycJeu+7AzbV/xhDMVtUx18Lcvul07QTzdO1GCdoB2gHaAbEQZxTXDtcOiR8hC4wSjBKxEAAAlfgp8b7pCObj553eLeQp8Sb+tQNCFmAupjczSp5RWEh5U8RPXDvPKIkfIQtw+pnrd+Cg0cLcoNE1ylbV59p703fgCOad3lLi4+fj5y3kBPOZ6wTz2gEAANoBIQuMErUDRgmQBUv8BPO+6cLcoNFayH/GFL+otzXKNcoI5pnr/AyJHzc9WEh1YLxpcm2bXi9XWEjP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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
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              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
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          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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            "text/plain": [
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              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Listening to some examples: only from the first microphone\n",
        "for i in range(0,10):\n",
        "  ipd.display(ipd.Audio(audioMatrix[:,0,i], rate=sr))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing some examples: only from the first microphone\n",
        "plt.figure(figsize=(15, 10))\n",
        "time_axis = np.arange(0,sr*duration) / sr\n",
        "for i in range(0, 10):\n",
        "  plt.subplot(5, 2, i+1)\n",
        "  plt.plot(time_axis, audioMatrix[:,0,i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "T6T6FUfnLKab",
        "outputId": "9a8d3368-6fec-4ce7-e96e-42650226cace"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yt91H9Kvu6VB",
        "outputId": "eeb5bed1-d029-4d34-a92e-c7f31655355b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 914
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/librosa/util/utils.py:200: UserWarning: librosa.util.frame called with axis=-1 on a non-contiguous input. This will result in a copy.\n",
            "  \"on a non-contiguous input. This will result in a copy.\".format(axis)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing SI estimated for the first sentence\n",
        "gamma = featureExtractor2(audioMatrix[:,:,0])\n",
        "\n",
        "plt.figure(figsize=(18, 18))\n",
        "for i in range(0, 10):\n",
        "  plt.subplot(5, 2, i+1)\n",
        "  # Converion to db is perfomed only for better visualization\n",
        "  librosa.display.specshow(librosa.amplitude_to_db(gamma[:,:,i]), sr=sr, x_axis='time', y_axis='linear', cmap='viridis')\n",
        "  plt.colorbar();\n",
        "  plt.clim(np.max(librosa.amplitude_to_db(gamma[:,:,i])), np.min(librosa.amplitude_to_db(gamma[:,:,i])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1o5eaJ6u6VC",
        "outputId": "02333800-87a8-4d1d-e79e-52b7cf8ca9ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(14, 511, 10)\n"
          ]
        }
      ],
      "source": [
        "print(gamma.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Training"
      ],
      "metadata": {
        "id": "AEihMIwsAawq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PLEASE NOTE:** this section ran on a local machine, we report it here just to show the code and the training curves (loss and accuracy)."
      ],
      "metadata": {
        "id": "a67x5dCIAlzt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting features from the training dataset:"
      ],
      "metadata": {
        "id": "sfvYiMa5SIdA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get list of audio files in the train dataset\n",
        "audiofiles_train = [str(file) for file in Path().glob('SA*.wav')]\n",
        "audiofiles_train = sorted_nicely(audiofiles_train)\n",
        "\n",
        "sr = 16000\n",
        "duration = 0.5\n",
        "\n",
        "# Extract audio and labels\n",
        "trainAudioMatrix, sourceIDs, labels30, labels10 = extractAudiodata(audiofiles_train, sr*duration)\n",
        "\n",
        "# Init gamma\n",
        "datapointsNum = trainAudioMatrix.shape[-1]\n",
        "Gamma_container = np.zeros([datapointsNum, 14, 511, 10])\n",
        "\n",
        "# Extract SI features\n",
        "for i in tqdm(range(0, datapointsNum)):\n",
        "  Gamma_container[i,:,:,:] = featureExtractor2(trainAudioMatrix[:,:,i])\n",
        "\n",
        "# Casting labels as integers\n",
        "intLabels10 = np.array(labels10, dtype='uint8')\n",
        "intLabels30 = np.array(labels30, dtype='uint8')"
      ],
      "metadata": {
        "id": "JM8Y9ndi2t-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting features from the validation dataset:"
      ],
      "metadata": {
        "id": "HVjj5JAO2RQK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GDFXdi_u6VH"
      },
      "outputs": [],
      "source": [
        "# Get list of audio files in the validation dataset\n",
        "audiofiles_val = [str(file) for file in Path().glob('SA*.wav')]\n",
        "audiofiles_val = sorted_nicely(audiofiles_val)\n",
        "\n",
        "sr = 16000\n",
        "duration = 0.5\n",
        "\n",
        "# Extract audio and labels\n",
        "valAudioMatrix, sourceIDs, valLabels30, valLabels10 = extractAudiodata(audiofiles_val, sr*duration)\n",
        "\n",
        "# Init gamma\n",
        "datapointsNum = valAudioMatrix.shape[-1]\n",
        "Gamma_container_val = np.zeros([datapointsNum, 14, 511, 10])\n",
        "\n",
        "# Extract SI features\n",
        "for i in tqdm(range(0, datapointsNum)):\n",
        "  Gamma_container_val[i,:,:,:] = featureExtractor2(valAudioMatrix[:,:,i])\n",
        "\n",
        "# Casting labels as integers\n",
        "intValLabels10 = np.array(valLabels10, dtype='uint8')\n",
        "intValLabels30 = np.array(valLabels30, dtype='uint8')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Model for resolution 10°"
      ],
      "metadata": {
        "id": "m8tTQM-7A6bd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Network architecture with K = 36 output classes"
      ],
      "metadata": {
        "id": "qSeASOGrTXO5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kPHRXn3uu6VF",
        "outputId": "ab4d52cd-9075-42c2-8392-c82df7806218"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1 (Conv2D)               (None, 12, 509, 64)       5824      \n",
            "_________________________________________________________________\n",
            "bn1 (BatchNormalization)     (None, 12, 509, 64)       256       \n",
            "_________________________________________________________________\n",
            "conv2 (Conv2D)               (None, 10, 507, 64)       36928     \n",
            "_________________________________________________________________\n",
            "bn2 (BatchNormalization)     (None, 10, 507, 64)       256       \n",
            "_________________________________________________________________\n",
            "dn1 (Dropout)                (None, 10, 507, 64)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 324480)            0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 512)               166134272 \n",
            "_________________________________________________________________\n",
            "dn2 (Dropout)                (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dn3 (Dropout)                (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 36)                18468     \n",
            "=================================================================\n",
            "Total params: 166,458,660\n",
            "Trainable params: 166,458,404\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "filters = 64\n",
        "kernel_size = (3,3)\n",
        "strides = (1,1)\n",
        "input_shape = (14, 511, 10)\n",
        "rate = 0.5\n",
        "K = 36\n",
        "\n",
        "model = keras.Sequential ([\n",
        "  # input layer (14 * 511 * 10) (convolutional layers + batch normalization (BN) w ReLU)\n",
        "  keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, input_shape=input_shape, activation='relu', name='conv1'),\n",
        "  keras.layers.BatchNormalization(name = 'bn1'),\n",
        "\n",
        "  # 2nd convolutional layers + batch normalization (BN) w ReLU\n",
        "  keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, activation='relu', name='conv2'),\n",
        "  keras.layers.BatchNormalization(name = 'bn2'),\n",
        "  # dropout procedure with rate 0.5\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn1'),\n",
        "\n",
        "  # 1st fully connected layer w ReLU & dropout procedure with rate 0.5\n",
        "  keras.layers.Flatten(name='flatten'),\n",
        "  tf.keras.layers.Dense(512, activation = 'relu', name = 'fc1'),\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn2'),\n",
        "\n",
        "  # 2nd fully connected layer w ReLU & dropout procedure with rate 0.5\n",
        "  tf.keras.layers.Dense(512, activation = 'relu', name = 'fc2'),\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn3'),\n",
        "\n",
        "  # Output layer\n",
        "  tf.keras.layers.Dense(K, activation = 'softmax', name = 'output'),\n",
        "])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fWFIdA7fu6VF"
      },
      "outputs": [],
      "source": [
        "# Compile\n",
        "learning_rate = 1e-5 \n",
        "optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6mBIqJcu6VH"
      },
      "outputs": [],
      "source": [
        "# Callbacks\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath='best_model',\n",
        "    save_weights_only=True,\n",
        "    monitor='val_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True)\n",
        "\n",
        "\n",
        "callbacks = [\n",
        "  model_checkpoint_callback,\n",
        "  tf.keras.callbacks.EarlyStopping(patience=25, monitor='accuracy', mode='max'),\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ekATdRtPu6VH"
      },
      "outputs": [],
      "source": [
        "# Shuffling the features\n",
        "(Gamma_container, intLabels10) = shuffle(Gamma_container, intLabels10)\n",
        "(Gamma_container_val, intValLabels10) = shuffle(Gamma_container_val, intValLabels10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2gLFwdyTu6VI",
        "outputId": "e6966ab0-b153-40c4-fdf8-90b344283df7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "188/188 [==============================] - 16s 75ms/step - loss: 3.5255 - accuracy: 0.0870 - val_loss: 3.5816 - val_accuracy: 0.0270\n",
            "Epoch 2/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 3.1264 - accuracy: 0.1370 - val_loss: 3.5793 - val_accuracy: 0.0470\n",
            "Epoch 3/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 2.7619 - accuracy: 0.1760 - val_loss: 3.4827 - val_accuracy: 0.0440\n",
            "Epoch 4/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 2.5418 - accuracy: 0.2138 - val_loss: 7.0159 - val_accuracy: 0.0270\n",
            "Epoch 5/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 2.4125 - accuracy: 0.2318 - val_loss: 5.5088 - val_accuracy: 0.0710\n",
            "Epoch 6/400\n",
            "188/188 [==============================] - 15s 76ms/step - loss: 2.3297 - accuracy: 0.2465 - val_loss: 3.9780 - val_accuracy: 0.0950\n",
            "Epoch 7/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 2.2243 - accuracy: 0.2842 - val_loss: 5.5266 - val_accuracy: 0.0350\n",
            "Epoch 8/400\n",
            "188/188 [==============================] - 15s 78ms/step - loss: 2.1546 - accuracy: 0.2993 - val_loss: 6.1912 - val_accuracy: 0.0610\n",
            "Epoch 9/400\n",
            "188/188 [==============================] - 15s 78ms/step - loss: 2.0394 - accuracy: 0.3362 - val_loss: 4.9140 - val_accuracy: 0.0250\n",
            "Epoch 10/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.9888 - accuracy: 0.3458 - val_loss: 1.8827 - val_accuracy: 0.3590\n",
            "Epoch 11/400\n",
            "188/188 [==============================] - 15s 77ms/step - loss: 1.8935 - accuracy: 0.3768 - val_loss: 3.0884 - val_accuracy: 0.1590\n",
            "Epoch 12/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.8103 - accuracy: 0.3995 - val_loss: 2.9549 - val_accuracy: 0.1560\n",
            "Epoch 13/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.7323 - accuracy: 0.4292 - val_loss: 2.4149 - val_accuracy: 0.2100\n",
            "Epoch 14/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.6387 - accuracy: 0.4607 - val_loss: 2.2784 - val_accuracy: 0.2460\n",
            "Epoch 15/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.5430 - accuracy: 0.4905 - val_loss: 1.8764 - val_accuracy: 0.4480\n",
            "Epoch 16/400\n",
            "188/188 [==============================] - 15s 77ms/step - loss: 1.5044 - accuracy: 0.5100 - val_loss: 2.3564 - val_accuracy: 0.2800\n",
            "Epoch 17/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 1.4058 - accuracy: 0.5412 - val_loss: 1.7121 - val_accuracy: 0.4910\n",
            "Epoch 18/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 1.3231 - accuracy: 0.5700 - val_loss: 3.0599 - val_accuracy: 0.1720\n",
            "Epoch 19/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 1.2548 - accuracy: 0.6038 - val_loss: 2.5999 - val_accuracy: 0.1790\n",
            "Epoch 20/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 1.1874 - accuracy: 0.6162 - val_loss: 3.1310 - val_accuracy: 0.1090\n",
            "Epoch 21/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 1.1016 - accuracy: 0.6500 - val_loss: 1.9106 - val_accuracy: 0.4630\n",
            "Epoch 22/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 1.0496 - accuracy: 0.6678 - val_loss: 1.7893 - val_accuracy: 0.4230\n",
            "Epoch 23/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.9804 - accuracy: 0.6958 - val_loss: 1.7091 - val_accuracy: 0.5090\n",
            "Epoch 24/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.9426 - accuracy: 0.7080 - val_loss: 1.3901 - val_accuracy: 0.5680\n",
            "Epoch 25/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.8658 - accuracy: 0.7333 - val_loss: 2.5488 - val_accuracy: 0.2210\n",
            "Epoch 26/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.8360 - accuracy: 0.7367 - val_loss: 1.4796 - val_accuracy: 0.6380\n",
            "Epoch 27/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.7816 - accuracy: 0.7597 - val_loss: 0.9866 - val_accuracy: 0.7240\n",
            "Epoch 28/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.7297 - accuracy: 0.7782 - val_loss: 1.5126 - val_accuracy: 0.5910\n",
            "Epoch 29/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.7188 - accuracy: 0.7808 - val_loss: 0.7285 - val_accuracy: 0.7930\n",
            "Epoch 30/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.6667 - accuracy: 0.8023 - val_loss: 1.1005 - val_accuracy: 0.6950\n",
            "Epoch 31/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.6399 - accuracy: 0.8065 - val_loss: 1.3761 - val_accuracy: 0.6340\n",
            "Epoch 32/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.6094 - accuracy: 0.8167 - val_loss: 1.1864 - val_accuracy: 0.7660\n",
            "Epoch 33/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.5918 - accuracy: 0.8258 - val_loss: 0.5292 - val_accuracy: 0.8190\n",
            "Epoch 34/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.5541 - accuracy: 0.8387 - val_loss: 1.3176 - val_accuracy: 0.5960\n",
            "Epoch 35/400\n",
            "188/188 [==============================] - 14s 74ms/step - loss: 0.5335 - accuracy: 0.8440 - val_loss: 0.9312 - val_accuracy: 0.7560\n",
            "Epoch 36/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.5003 - accuracy: 0.8537 - val_loss: 1.1582 - val_accuracy: 0.6440\n",
            "Epoch 37/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.4711 - accuracy: 0.8685 - val_loss: 0.8770 - val_accuracy: 0.7320\n",
            "Epoch 38/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.4602 - accuracy: 0.8698 - val_loss: 1.0248 - val_accuracy: 0.7030\n",
            "Epoch 39/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.4493 - accuracy: 0.8730 - val_loss: 0.6805 - val_accuracy: 0.8570\n",
            "Epoch 40/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.4285 - accuracy: 0.8758 - val_loss: 0.8983 - val_accuracy: 0.7060\n",
            "Epoch 41/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3986 - accuracy: 0.8888 - val_loss: 0.4831 - val_accuracy: 0.8710\n",
            "Epoch 42/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3903 - accuracy: 0.8867 - val_loss: 0.5260 - val_accuracy: 0.8880\n",
            "Epoch 43/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3685 - accuracy: 0.8905 - val_loss: 0.3126 - val_accuracy: 0.9140\n",
            "Epoch 44/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3595 - accuracy: 0.9032 - val_loss: 0.3420 - val_accuracy: 0.9020\n",
            "Epoch 45/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3541 - accuracy: 0.8977 - val_loss: 0.6288 - val_accuracy: 0.8310\n",
            "Epoch 46/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3373 - accuracy: 0.9058 - val_loss: 0.3821 - val_accuracy: 0.8890\n",
            "Epoch 47/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3306 - accuracy: 0.9050 - val_loss: 0.5476 - val_accuracy: 0.8510\n",
            "Epoch 48/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3111 - accuracy: 0.9133 - val_loss: 0.3445 - val_accuracy: 0.8990\n",
            "Epoch 49/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.3125 - accuracy: 0.9135 - val_loss: 0.3086 - val_accuracy: 0.9160\n",
            "Epoch 50/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2898 - accuracy: 0.9187 - val_loss: 0.4826 - val_accuracy: 0.8550\n",
            "Epoch 51/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2807 - accuracy: 0.9213 - val_loss: 0.4527 - val_accuracy: 0.9110\n",
            "Epoch 52/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2792 - accuracy: 0.9238 - val_loss: 0.6872 - val_accuracy: 0.7850\n",
            "Epoch 53/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2628 - accuracy: 0.9233 - val_loss: 0.4826 - val_accuracy: 0.9110\n",
            "Epoch 54/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2555 - accuracy: 0.9268 - val_loss: 0.5125 - val_accuracy: 0.8790\n",
            "Epoch 55/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2416 - accuracy: 0.9352 - val_loss: 0.5041 - val_accuracy: 0.8890\n",
            "Epoch 56/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2440 - accuracy: 0.9337 - val_loss: 1.0063 - val_accuracy: 0.7020\n",
            "Epoch 57/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2472 - accuracy: 0.9323 - val_loss: 0.5593 - val_accuracy: 0.8190\n",
            "Epoch 58/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2259 - accuracy: 0.9398 - val_loss: 0.2922 - val_accuracy: 0.9090\n",
            "Epoch 59/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2284 - accuracy: 0.9362 - val_loss: 0.3724 - val_accuracy: 0.8910\n",
            "Epoch 60/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2150 - accuracy: 0.9427 - val_loss: 0.3008 - val_accuracy: 0.9170\n",
            "Epoch 61/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2127 - accuracy: 0.9445 - val_loss: 0.2856 - val_accuracy: 0.9230\n",
            "Epoch 62/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2007 - accuracy: 0.9465 - val_loss: 0.4879 - val_accuracy: 0.8750\n",
            "Epoch 63/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.2029 - accuracy: 0.9458 - val_loss: 0.5249 - val_accuracy: 0.8420\n",
            "Epoch 64/400\n",
            "188/188 [==============================] - 14s 74ms/step - loss: 0.2067 - accuracy: 0.9425 - val_loss: 0.8029 - val_accuracy: 0.7500\n",
            "Epoch 65/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1905 - accuracy: 0.9507 - val_loss: 0.3657 - val_accuracy: 0.8880\n",
            "Epoch 66/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1890 - accuracy: 0.9513 - val_loss: 0.3262 - val_accuracy: 0.9210\n",
            "Epoch 67/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1853 - accuracy: 0.9522 - val_loss: 0.2614 - val_accuracy: 0.9190\n",
            "Epoch 68/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1797 - accuracy: 0.9533 - val_loss: 0.2174 - val_accuracy: 0.9260\n",
            "Epoch 69/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1763 - accuracy: 0.9535 - val_loss: 0.6363 - val_accuracy: 0.8100\n",
            "Epoch 70/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1722 - accuracy: 0.9547 - val_loss: 0.2690 - val_accuracy: 0.9200\n",
            "Epoch 71/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1732 - accuracy: 0.9547 - val_loss: 0.6376 - val_accuracy: 0.8040\n",
            "Epoch 72/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1688 - accuracy: 0.9560 - val_loss: 0.5758 - val_accuracy: 0.8150\n",
            "Epoch 73/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1740 - accuracy: 0.9547 - val_loss: 0.3214 - val_accuracy: 0.9160\n",
            "Epoch 74/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1651 - accuracy: 0.9587 - val_loss: 0.5534 - val_accuracy: 0.8370\n",
            "Epoch 75/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1587 - accuracy: 0.9590 - val_loss: 0.3735 - val_accuracy: 0.8790\n",
            "Epoch 76/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1691 - accuracy: 0.9545 - val_loss: 0.4770 - val_accuracy: 0.8580\n",
            "Epoch 77/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1593 - accuracy: 0.9595 - val_loss: 0.3201 - val_accuracy: 0.9040\n",
            "Epoch 78/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1661 - accuracy: 0.9597 - val_loss: 0.2726 - val_accuracy: 0.9160\n",
            "Epoch 79/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1586 - accuracy: 0.9592 - val_loss: 0.2867 - val_accuracy: 0.9120\n",
            "Epoch 80/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1583 - accuracy: 0.9613 - val_loss: 0.7392 - val_accuracy: 0.7810\n",
            "Epoch 81/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1492 - accuracy: 0.9648 - val_loss: 0.4013 - val_accuracy: 0.8810\n",
            "Epoch 82/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1558 - accuracy: 0.9593 - val_loss: 0.2662 - val_accuracy: 0.9280\n",
            "Epoch 83/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1512 - accuracy: 0.9607 - val_loss: 0.2806 - val_accuracy: 0.9280\n",
            "Epoch 84/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1418 - accuracy: 0.9665 - val_loss: 0.6933 - val_accuracy: 0.7990\n",
            "Epoch 85/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1410 - accuracy: 0.9660 - val_loss: 0.3092 - val_accuracy: 0.9090\n",
            "Epoch 86/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1450 - accuracy: 0.9632 - val_loss: 0.2574 - val_accuracy: 0.9340\n",
            "Epoch 87/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1345 - accuracy: 0.9655 - val_loss: 0.2659 - val_accuracy: 0.9310\n",
            "Epoch 88/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1407 - accuracy: 0.9630 - val_loss: 0.6620 - val_accuracy: 0.8160\n",
            "Epoch 89/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1265 - accuracy: 0.9690 - val_loss: 0.2928 - val_accuracy: 0.9270\n",
            "Epoch 90/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1251 - accuracy: 0.9690 - val_loss: 0.3107 - val_accuracy: 0.9290\n",
            "Epoch 91/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1311 - accuracy: 0.9657 - val_loss: 0.5493 - val_accuracy: 0.8330\n",
            "Epoch 92/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1214 - accuracy: 0.9702 - val_loss: 0.2032 - val_accuracy: 0.9310\n",
            "Epoch 93/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1175 - accuracy: 0.9722 - val_loss: 0.2716 - val_accuracy: 0.9220\n",
            "Epoch 94/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1198 - accuracy: 0.9678 - val_loss: 0.5010 - val_accuracy: 0.8490\n",
            "Epoch 95/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1200 - accuracy: 0.9698 - val_loss: 0.3481 - val_accuracy: 0.9010\n",
            "Epoch 96/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1252 - accuracy: 0.9685 - val_loss: 0.3306 - val_accuracy: 0.9110\n",
            "Epoch 97/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1167 - accuracy: 0.9710 - val_loss: 0.4238 - val_accuracy: 0.8670\n",
            "Epoch 98/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1117 - accuracy: 0.9727 - val_loss: 0.2149 - val_accuracy: 0.9320\n",
            "Epoch 99/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1148 - accuracy: 0.9738 - val_loss: 0.3345 - val_accuracy: 0.8880\n",
            "Epoch 100/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1074 - accuracy: 0.9725 - val_loss: 0.3747 - val_accuracy: 0.9040\n",
            "Epoch 101/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1076 - accuracy: 0.9740 - val_loss: 0.2262 - val_accuracy: 0.9270\n",
            "Epoch 102/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1074 - accuracy: 0.9715 - val_loss: 0.2018 - val_accuracy: 0.9340\n",
            "Epoch 103/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1072 - accuracy: 0.9732 - val_loss: 0.3816 - val_accuracy: 0.9160\n",
            "Epoch 104/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1034 - accuracy: 0.9750 - val_loss: 0.5631 - val_accuracy: 0.8620\n",
            "Epoch 105/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0991 - accuracy: 0.9763 - val_loss: 0.1832 - val_accuracy: 0.9430\n",
            "Epoch 106/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1009 - accuracy: 0.9723 - val_loss: 0.2055 - val_accuracy: 0.9440\n",
            "Epoch 107/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0993 - accuracy: 0.9758 - val_loss: 0.3114 - val_accuracy: 0.9090\n",
            "Epoch 108/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0971 - accuracy: 0.9748 - val_loss: 0.3212 - val_accuracy: 0.9050\n",
            "Epoch 109/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0932 - accuracy: 0.9772 - val_loss: 0.2395 - val_accuracy: 0.9220\n",
            "Epoch 110/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0971 - accuracy: 0.9750 - val_loss: 0.2256 - val_accuracy: 0.9280\n",
            "Epoch 111/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0940 - accuracy: 0.9787 - val_loss: 0.3674 - val_accuracy: 0.8870\n",
            "Epoch 112/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0943 - accuracy: 0.9770 - val_loss: 0.4682 - val_accuracy: 0.8630\n",
            "Epoch 113/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0929 - accuracy: 0.9792 - val_loss: 0.3087 - val_accuracy: 0.9220\n",
            "Epoch 114/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0918 - accuracy: 0.9787 - val_loss: 0.2577 - val_accuracy: 0.9240\n",
            "Epoch 115/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0911 - accuracy: 0.9788 - val_loss: 0.2998 - val_accuracy: 0.9350\n",
            "Epoch 116/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0963 - accuracy: 0.9780 - val_loss: 0.5242 - val_accuracy: 0.8370\n",
            "Epoch 117/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0957 - accuracy: 0.9732 - val_loss: 0.2543 - val_accuracy: 0.9280\n",
            "Epoch 118/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0989 - accuracy: 0.9767 - val_loss: 0.2704 - val_accuracy: 0.9160\n",
            "Epoch 119/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.1009 - accuracy: 0.9757 - val_loss: 0.2949 - val_accuracy: 0.9040\n",
            "Epoch 120/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0947 - accuracy: 0.9767 - val_loss: 0.2478 - val_accuracy: 0.9240\n",
            "Epoch 121/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0918 - accuracy: 0.9788 - val_loss: 0.3725 - val_accuracy: 0.8900\n",
            "Epoch 122/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0915 - accuracy: 0.9780 - val_loss: 0.2628 - val_accuracy: 0.9210\n",
            "Epoch 123/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0924 - accuracy: 0.9773 - val_loss: 0.4685 - val_accuracy: 0.8520\n",
            "Epoch 124/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0879 - accuracy: 0.9772 - val_loss: 0.3513 - val_accuracy: 0.9030\n",
            "Epoch 125/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0849 - accuracy: 0.9812 - val_loss: 0.4297 - val_accuracy: 0.8730\n",
            "Epoch 126/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0844 - accuracy: 0.9805 - val_loss: 0.6828 - val_accuracy: 0.8160\n",
            "Epoch 127/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0864 - accuracy: 0.9800 - val_loss: 0.1802 - val_accuracy: 0.9420\n",
            "Epoch 128/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0806 - accuracy: 0.9813 - val_loss: 0.3021 - val_accuracy: 0.9070\n",
            "Epoch 129/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0932 - accuracy: 0.9768 - val_loss: 0.1983 - val_accuracy: 0.9450\n",
            "Epoch 130/400\n",
            "188/188 [==============================] - 15s 77ms/step - loss: 0.0849 - accuracy: 0.9805 - val_loss: 0.2681 - val_accuracy: 0.9330\n",
            "Epoch 131/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0841 - accuracy: 0.9815 - val_loss: 0.2528 - val_accuracy: 0.9200\n",
            "Epoch 132/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0846 - accuracy: 0.9797 - val_loss: 0.4654 - val_accuracy: 0.8720\n",
            "Epoch 133/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0833 - accuracy: 0.9807 - val_loss: 0.2241 - val_accuracy: 0.9340\n",
            "Epoch 134/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0792 - accuracy: 0.9817 - val_loss: 0.4545 - val_accuracy: 0.8620\n",
            "Epoch 135/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0786 - accuracy: 0.9808 - val_loss: 0.2817 - val_accuracy: 0.9170\n",
            "Epoch 136/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0847 - accuracy: 0.9833 - val_loss: 0.2173 - val_accuracy: 0.9380\n",
            "Epoch 137/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0829 - accuracy: 0.9808 - val_loss: 0.3853 - val_accuracy: 0.8850\n",
            "Epoch 138/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0863 - accuracy: 0.9797 - val_loss: 0.2726 - val_accuracy: 0.9350\n",
            "Epoch 139/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0823 - accuracy: 0.9805 - val_loss: 0.3305 - val_accuracy: 0.9080\n",
            "Epoch 140/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0799 - accuracy: 0.9827 - val_loss: 0.4187 - val_accuracy: 0.8700\n",
            "Epoch 141/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0767 - accuracy: 0.9825 - val_loss: 0.3852 - val_accuracy: 0.9000\n",
            "Epoch 142/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0770 - accuracy: 0.9813 - val_loss: 0.3456 - val_accuracy: 0.9040\n",
            "Epoch 143/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0751 - accuracy: 0.9810 - val_loss: 0.7207 - val_accuracy: 0.8370\n",
            "Epoch 144/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0752 - accuracy: 0.9837 - val_loss: 0.4057 - val_accuracy: 0.8740\n",
            "Epoch 145/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0705 - accuracy: 0.9840 - val_loss: 0.5167 - val_accuracy: 0.8770\n",
            "Epoch 146/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0737 - accuracy: 0.9813 - val_loss: 0.6497 - val_accuracy: 0.8370\n",
            "Epoch 147/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0725 - accuracy: 0.9830 - val_loss: 0.1757 - val_accuracy: 0.9500\n",
            "Epoch 148/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0745 - accuracy: 0.9838 - val_loss: 0.1882 - val_accuracy: 0.9470\n",
            "Epoch 149/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0715 - accuracy: 0.9835 - val_loss: 0.4381 - val_accuracy: 0.8660\n",
            "Epoch 150/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0730 - accuracy: 0.9818 - val_loss: 0.2815 - val_accuracy: 0.9120\n",
            "Epoch 151/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0680 - accuracy: 0.9857 - val_loss: 0.2081 - val_accuracy: 0.9320\n",
            "Epoch 152/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0682 - accuracy: 0.9842 - val_loss: 0.1981 - val_accuracy: 0.9420\n",
            "Epoch 153/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0675 - accuracy: 0.9843 - val_loss: 0.3687 - val_accuracy: 0.9220\n",
            "Epoch 154/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0713 - accuracy: 0.9815 - val_loss: 0.2657 - val_accuracy: 0.9300\n",
            "Epoch 155/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0673 - accuracy: 0.9850 - val_loss: 0.2103 - val_accuracy: 0.9400\n",
            "Epoch 156/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0684 - accuracy: 0.9853 - val_loss: 0.5502 - val_accuracy: 0.8610\n",
            "Epoch 157/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0722 - accuracy: 0.9820 - val_loss: 0.2013 - val_accuracy: 0.9410\n",
            "Epoch 158/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0784 - accuracy: 0.9812 - val_loss: 0.2321 - val_accuracy: 0.9280\n",
            "Epoch 159/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0711 - accuracy: 0.9825 - val_loss: 0.5169 - val_accuracy: 0.8530\n",
            "Epoch 160/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0686 - accuracy: 0.9848 - val_loss: 0.2223 - val_accuracy: 0.9380\n",
            "Epoch 161/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0643 - accuracy: 0.9867 - val_loss: 0.2521 - val_accuracy: 0.9260\n",
            "Epoch 162/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0673 - accuracy: 0.9830 - val_loss: 0.4939 - val_accuracy: 0.8690\n",
            "Epoch 163/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0627 - accuracy: 0.9843 - val_loss: 0.4670 - val_accuracy: 0.8930\n",
            "Epoch 164/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0603 - accuracy: 0.9863 - val_loss: 0.3913 - val_accuracy: 0.8980\n",
            "Epoch 165/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0594 - accuracy: 0.9870 - val_loss: 0.3854 - val_accuracy: 0.8900\n",
            "Epoch 166/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0645 - accuracy: 0.9838 - val_loss: 0.2344 - val_accuracy: 0.9330\n",
            "Epoch 167/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0621 - accuracy: 0.9860 - val_loss: 0.2201 - val_accuracy: 0.9380\n",
            "Epoch 168/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0621 - accuracy: 0.9858 - val_loss: 0.3452 - val_accuracy: 0.9080\n",
            "Epoch 169/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0616 - accuracy: 0.9843 - val_loss: 0.1966 - val_accuracy: 0.9430\n",
            "Epoch 170/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0611 - accuracy: 0.9868 - val_loss: 0.2897 - val_accuracy: 0.9100\n",
            "Epoch 171/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0631 - accuracy: 0.9873 - val_loss: 0.2084 - val_accuracy: 0.9300\n",
            "Epoch 172/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0614 - accuracy: 0.9863 - val_loss: 0.2294 - val_accuracy: 0.9360\n",
            "Epoch 173/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0580 - accuracy: 0.9880 - val_loss: 0.3028 - val_accuracy: 0.9160\n",
            "Epoch 174/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0599 - accuracy: 0.9863 - val_loss: 0.2407 - val_accuracy: 0.9430\n",
            "Epoch 175/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0549 - accuracy: 0.9898 - val_loss: 0.3198 - val_accuracy: 0.9150\n",
            "Epoch 176/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0554 - accuracy: 0.9888 - val_loss: 0.3451 - val_accuracy: 0.9100\n",
            "Epoch 177/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0586 - accuracy: 0.9870 - val_loss: 0.1706 - val_accuracy: 0.9520\n",
            "Epoch 178/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0596 - accuracy: 0.9863 - val_loss: 0.2012 - val_accuracy: 0.9450\n",
            "Epoch 179/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0582 - accuracy: 0.9867 - val_loss: 0.2948 - val_accuracy: 0.9160\n",
            "Epoch 180/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0545 - accuracy: 0.9892 - val_loss: 0.4491 - val_accuracy: 0.9080\n",
            "Epoch 181/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0590 - accuracy: 0.9860 - val_loss: 1.1455 - val_accuracy: 0.7140\n",
            "Epoch 182/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0561 - accuracy: 0.9875 - val_loss: 0.2567 - val_accuracy: 0.9230\n",
            "Epoch 183/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0544 - accuracy: 0.9877 - val_loss: 0.9085 - val_accuracy: 0.7810\n",
            "Epoch 184/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0544 - accuracy: 0.9893 - val_loss: 0.4196 - val_accuracy: 0.8980\n",
            "Epoch 185/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0529 - accuracy: 0.9888 - val_loss: 0.2822 - val_accuracy: 0.9160\n",
            "Epoch 186/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0480 - accuracy: 0.9910 - val_loss: 0.1527 - val_accuracy: 0.9500\n",
            "Epoch 187/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0543 - accuracy: 0.9877 - val_loss: 0.3330 - val_accuracy: 0.9060\n",
            "Epoch 188/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0507 - accuracy: 0.9893 - val_loss: 0.1951 - val_accuracy: 0.9460\n",
            "Epoch 189/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.2767 - val_accuracy: 0.9340\n",
            "Epoch 190/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0515 - accuracy: 0.9867 - val_loss: 0.2426 - val_accuracy: 0.9220\n",
            "Epoch 191/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0468 - accuracy: 0.9898 - val_loss: 0.3583 - val_accuracy: 0.9140\n",
            "Epoch 192/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0498 - accuracy: 0.9895 - val_loss: 0.3136 - val_accuracy: 0.9110\n",
            "Epoch 193/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0492 - accuracy: 0.9882 - val_loss: 0.8537 - val_accuracy: 0.8220\n",
            "Epoch 194/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0500 - accuracy: 0.9882 - val_loss: 0.5230 - val_accuracy: 0.8810\n",
            "Epoch 195/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0459 - accuracy: 0.9903 - val_loss: 0.3959 - val_accuracy: 0.8880\n",
            "Epoch 196/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0448 - accuracy: 0.9898 - val_loss: 0.2958 - val_accuracy: 0.9240\n",
            "Epoch 197/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0475 - accuracy: 0.9893 - val_loss: 0.2089 - val_accuracy: 0.9420\n",
            "Epoch 198/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0437 - accuracy: 0.9908 - val_loss: 0.1686 - val_accuracy: 0.9450\n",
            "Epoch 199/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0436 - accuracy: 0.9905 - val_loss: 0.3822 - val_accuracy: 0.9070\n",
            "Epoch 200/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0418 - accuracy: 0.9905 - val_loss: 2.1636 - val_accuracy: 0.7120\n",
            "Epoch 201/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0420 - accuracy: 0.9900 - val_loss: 0.2399 - val_accuracy: 0.9370\n",
            "Epoch 202/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0419 - accuracy: 0.9913 - val_loss: 0.1720 - val_accuracy: 0.9500\n",
            "Epoch 203/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0460 - accuracy: 0.9892 - val_loss: 0.2861 - val_accuracy: 0.9120\n",
            "Epoch 204/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0427 - accuracy: 0.9908 - val_loss: 0.2839 - val_accuracy: 0.9240\n",
            "Epoch 205/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0420 - accuracy: 0.9898 - val_loss: 0.1839 - val_accuracy: 0.9470\n",
            "Epoch 206/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0650 - accuracy: 0.9867 - val_loss: 0.5864 - val_accuracy: 0.8420\n",
            "Epoch 207/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.3041 - val_accuracy: 0.9120\n",
            "Epoch 208/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0553 - accuracy: 0.9888 - val_loss: 0.1803 - val_accuracy: 0.9480\n",
            "Epoch 209/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0559 - accuracy: 0.9857 - val_loss: 0.3038 - val_accuracy: 0.9210\n",
            "Epoch 210/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0538 - accuracy: 0.9895 - val_loss: 0.3408 - val_accuracy: 0.9210\n",
            "Epoch 211/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0515 - accuracy: 0.9897 - val_loss: 0.3088 - val_accuracy: 0.9010\n",
            "Epoch 212/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0521 - accuracy: 0.9893 - val_loss: 0.2474 - val_accuracy: 0.9250\n",
            "Epoch 213/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0492 - accuracy: 0.9898 - val_loss: 0.6815 - val_accuracy: 0.8580\n",
            "Epoch 214/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0470 - accuracy: 0.9910 - val_loss: 0.2793 - val_accuracy: 0.9270\n",
            "Epoch 215/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0455 - accuracy: 0.9897 - val_loss: 0.2682 - val_accuracy: 0.9180\n",
            "Epoch 216/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0481 - accuracy: 0.9892 - val_loss: 0.5378 - val_accuracy: 0.8600\n",
            "Epoch 217/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0464 - accuracy: 0.9905 - val_loss: 0.2173 - val_accuracy: 0.9410\n",
            "Epoch 218/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0447 - accuracy: 0.9902 - val_loss: 0.2767 - val_accuracy: 0.9300\n",
            "Epoch 219/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0481 - accuracy: 0.9885 - val_loss: 0.1855 - val_accuracy: 0.9500\n",
            "Epoch 220/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0473 - accuracy: 0.9907 - val_loss: 0.6499 - val_accuracy: 0.8670\n",
            "Epoch 221/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0495 - accuracy: 0.9893 - val_loss: 0.1513 - val_accuracy: 0.9580\n",
            "Epoch 222/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0459 - accuracy: 0.9910 - val_loss: 1.2556 - val_accuracy: 0.7790\n",
            "Epoch 223/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0422 - accuracy: 0.9910 - val_loss: 0.2569 - val_accuracy: 0.9260\n",
            "Epoch 224/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0425 - accuracy: 0.9908 - val_loss: 0.4773 - val_accuracy: 0.8880\n",
            "Epoch 225/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0414 - accuracy: 0.9917 - val_loss: 0.2555 - val_accuracy: 0.9300\n",
            "Epoch 226/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0437 - accuracy: 0.9910 - val_loss: 0.3657 - val_accuracy: 0.8980\n",
            "Epoch 227/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0400 - accuracy: 0.9908 - val_loss: 0.6000 - val_accuracy: 0.8760\n",
            "Epoch 228/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0377 - accuracy: 0.9918 - val_loss: 0.1733 - val_accuracy: 0.9490\n",
            "Epoch 229/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0379 - accuracy: 0.9912 - val_loss: 0.4738 - val_accuracy: 0.8880\n",
            "Epoch 230/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0410 - accuracy: 0.9922 - val_loss: 0.8037 - val_accuracy: 0.8340\n",
            "Epoch 231/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0399 - accuracy: 0.9915 - val_loss: 0.1634 - val_accuracy: 0.9590\n",
            "Epoch 232/400\n",
            "188/188 [==============================] - 15s 77ms/step - loss: 0.0351 - accuracy: 0.9923 - val_loss: 0.3218 - val_accuracy: 0.9040\n",
            "Epoch 233/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0353 - accuracy: 0.9933 - val_loss: 0.3017 - val_accuracy: 0.9240\n",
            "Epoch 234/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.4281 - val_accuracy: 0.8940\n",
            "Epoch 235/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0401 - accuracy: 0.9910 - val_loss: 0.5101 - val_accuracy: 0.8990\n",
            "Epoch 236/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0339 - accuracy: 0.9933 - val_loss: 0.1858 - val_accuracy: 0.9480\n",
            "Epoch 237/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0347 - accuracy: 0.9913 - val_loss: 0.1760 - val_accuracy: 0.9520\n",
            "Epoch 238/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0350 - accuracy: 0.9928 - val_loss: 0.2382 - val_accuracy: 0.9330\n",
            "Epoch 239/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0329 - accuracy: 0.9937 - val_loss: 0.2049 - val_accuracy: 0.9470\n",
            "Epoch 240/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0384 - accuracy: 0.9917 - val_loss: 0.2253 - val_accuracy: 0.9320\n",
            "Epoch 241/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0394 - accuracy: 0.9907 - val_loss: 0.2427 - val_accuracy: 0.9350\n",
            "Epoch 242/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0406 - accuracy: 0.9920 - val_loss: 0.7456 - val_accuracy: 0.8160\n",
            "Epoch 243/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0375 - accuracy: 0.9923 - val_loss: 0.1965 - val_accuracy: 0.9450\n",
            "Epoch 244/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0372 - accuracy: 0.9905 - val_loss: 0.2037 - val_accuracy: 0.9420\n",
            "Epoch 245/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0348 - accuracy: 0.9928 - val_loss: 0.2438 - val_accuracy: 0.9280\n",
            "Epoch 246/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0353 - accuracy: 0.9925 - val_loss: 0.2692 - val_accuracy: 0.9360\n",
            "Epoch 247/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0387 - accuracy: 0.9908 - val_loss: 0.2953 - val_accuracy: 0.9330\n",
            "Epoch 248/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0412 - accuracy: 0.9910 - val_loss: 0.1942 - val_accuracy: 0.9490\n",
            "Epoch 249/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0362 - accuracy: 0.9920 - val_loss: 0.3133 - val_accuracy: 0.9320\n",
            "Epoch 250/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0364 - accuracy: 0.9925 - val_loss: 0.3415 - val_accuracy: 0.9160\n",
            "Epoch 251/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0345 - accuracy: 0.9927 - val_loss: 0.5562 - val_accuracy: 0.8840\n",
            "Epoch 252/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0363 - accuracy: 0.9920 - val_loss: 0.2306 - val_accuracy: 0.9420\n",
            "Epoch 253/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0331 - accuracy: 0.9932 - val_loss: 0.3369 - val_accuracy: 0.9140\n",
            "Epoch 254/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0351 - accuracy: 0.9922 - val_loss: 0.9216 - val_accuracy: 0.8220\n",
            "Epoch 255/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0300 - accuracy: 0.9937 - val_loss: 0.1894 - val_accuracy: 0.9420\n",
            "Epoch 256/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0334 - accuracy: 0.9928 - val_loss: 0.2835 - val_accuracy: 0.9340\n",
            "Epoch 257/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0336 - accuracy: 0.9930 - val_loss: 0.5201 - val_accuracy: 0.8790\n",
            "Epoch 258/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0313 - accuracy: 0.9938 - val_loss: 0.1713 - val_accuracy: 0.9520\n",
            "Epoch 259/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0308 - accuracy: 0.9938 - val_loss: 0.6957 - val_accuracy: 0.8570\n",
            "Epoch 260/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0335 - accuracy: 0.9925 - val_loss: 0.3919 - val_accuracy: 0.9100\n",
            "Epoch 261/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0302 - accuracy: 0.9935 - val_loss: 0.2848 - val_accuracy: 0.9300\n",
            "Epoch 262/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0323 - accuracy: 0.9930 - val_loss: 0.1974 - val_accuracy: 0.9520\n",
            "Epoch 263/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0331 - accuracy: 0.9922 - val_loss: 0.1824 - val_accuracy: 0.9450\n",
            "Epoch 264/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0308 - accuracy: 0.9933 - val_loss: 0.3205 - val_accuracy: 0.9160\n",
            "Epoch 265/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0318 - accuracy: 0.9943 - val_loss: 1.6117 - val_accuracy: 0.7670\n",
            "Epoch 266/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0330 - accuracy: 0.9942 - val_loss: 0.2091 - val_accuracy: 0.9410\n",
            "Epoch 267/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0326 - accuracy: 0.9937 - val_loss: 0.2559 - val_accuracy: 0.9380\n",
            "Epoch 268/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0353 - accuracy: 0.9920 - val_loss: 0.2390 - val_accuracy: 0.9280\n",
            "Epoch 269/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0316 - accuracy: 0.9937 - val_loss: 0.3477 - val_accuracy: 0.9260\n",
            "Epoch 270/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0333 - accuracy: 0.9915 - val_loss: 0.5480 - val_accuracy: 0.8830\n",
            "Epoch 271/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0311 - accuracy: 0.9938 - val_loss: 0.3067 - val_accuracy: 0.9240\n",
            "Epoch 272/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0356 - accuracy: 0.9918 - val_loss: 0.1587 - val_accuracy: 0.9560\n",
            "Epoch 273/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0291 - accuracy: 0.9942 - val_loss: 0.3661 - val_accuracy: 0.9170\n",
            "Epoch 274/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0305 - accuracy: 0.9940 - val_loss: 0.1570 - val_accuracy: 0.9540\n",
            "Epoch 275/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0322 - accuracy: 0.9935 - val_loss: 0.1792 - val_accuracy: 0.9520\n",
            "Epoch 276/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0302 - accuracy: 0.9938 - val_loss: 0.3738 - val_accuracy: 0.8980\n",
            "Epoch 277/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0294 - accuracy: 0.9925 - val_loss: 1.4007 - val_accuracy: 0.7740\n",
            "Epoch 278/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0388 - accuracy: 0.9910 - val_loss: 0.9238 - val_accuracy: 0.8240\n",
            "Epoch 279/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0290 - accuracy: 0.9928 - val_loss: 0.2152 - val_accuracy: 0.9510\n",
            "Epoch 280/400\n",
            "188/188 [==============================] - 14s 76ms/step - loss: 0.0277 - accuracy: 0.9942 - val_loss: 0.4001 - val_accuracy: 0.9050\n",
            "Epoch 281/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0296 - accuracy: 0.9942 - val_loss: 0.1430 - val_accuracy: 0.9620\n",
            "Epoch 282/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0288 - accuracy: 0.9940 - val_loss: 0.3639 - val_accuracy: 0.8970\n",
            "Epoch 283/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0262 - accuracy: 0.9948 - val_loss: 0.1531 - val_accuracy: 0.9510\n",
            "Epoch 284/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0264 - accuracy: 0.9942 - val_loss: 0.2074 - val_accuracy: 0.9450\n",
            "Epoch 285/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0278 - accuracy: 0.9943 - val_loss: 0.3856 - val_accuracy: 0.9160\n",
            "Epoch 286/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0308 - accuracy: 0.9938 - val_loss: 0.5776 - val_accuracy: 0.8780\n",
            "Epoch 287/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0283 - accuracy: 0.9935 - val_loss: 0.3118 - val_accuracy: 0.9240\n",
            "Epoch 288/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0287 - accuracy: 0.9932 - val_loss: 0.5291 - val_accuracy: 0.9090\n",
            "Epoch 289/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0296 - accuracy: 0.9932 - val_loss: 0.1573 - val_accuracy: 0.9530\n",
            "Epoch 290/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0256 - accuracy: 0.9942 - val_loss: 0.3098 - val_accuracy: 0.9050\n",
            "Epoch 291/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0261 - accuracy: 0.9938 - val_loss: 0.1989 - val_accuracy: 0.9480\n",
            "Epoch 292/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0238 - accuracy: 0.9957 - val_loss: 0.2776 - val_accuracy: 0.9410\n",
            "Epoch 293/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0274 - accuracy: 0.9933 - val_loss: 0.2380 - val_accuracy: 0.9280\n",
            "Epoch 294/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0264 - accuracy: 0.9942 - val_loss: 0.4547 - val_accuracy: 0.8970\n",
            "Epoch 295/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0254 - accuracy: 0.9943 - val_loss: 0.2685 - val_accuracy: 0.9320\n",
            "Epoch 296/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0235 - accuracy: 0.9947 - val_loss: 0.1576 - val_accuracy: 0.9590\n",
            "Epoch 297/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0262 - accuracy: 0.9942 - val_loss: 0.2141 - val_accuracy: 0.9500\n",
            "Epoch 298/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0282 - accuracy: 0.9928 - val_loss: 0.2879 - val_accuracy: 0.9360\n",
            "Epoch 299/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0307 - accuracy: 0.9928 - val_loss: 0.2165 - val_accuracy: 0.9400\n",
            "Epoch 300/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0245 - accuracy: 0.9945 - val_loss: 0.2187 - val_accuracy: 0.9370\n",
            "Epoch 301/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0253 - accuracy: 0.9952 - val_loss: 0.2158 - val_accuracy: 0.9450\n",
            "Epoch 302/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0247 - accuracy: 0.9953 - val_loss: 0.1855 - val_accuracy: 0.9520\n",
            "Epoch 303/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0247 - accuracy: 0.9942 - val_loss: 0.1877 - val_accuracy: 0.9500\n",
            "Epoch 304/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0244 - accuracy: 0.9945 - val_loss: 0.2312 - val_accuracy: 0.9410\n",
            "Epoch 305/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0237 - accuracy: 0.9942 - val_loss: 0.1818 - val_accuracy: 0.9490\n",
            "Epoch 306/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0233 - accuracy: 0.9947 - val_loss: 0.9735 - val_accuracy: 0.8240\n",
            "Epoch 307/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0255 - accuracy: 0.9943 - val_loss: 0.1932 - val_accuracy: 0.9420\n",
            "Epoch 308/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0214 - accuracy: 0.9952 - val_loss: 0.2225 - val_accuracy: 0.9440\n",
            "Epoch 309/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0229 - accuracy: 0.9947 - val_loss: 0.1933 - val_accuracy: 0.9500\n",
            "Epoch 310/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0210 - accuracy: 0.9955 - val_loss: 0.1493 - val_accuracy: 0.9610\n",
            "Epoch 311/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0210 - accuracy: 0.9960 - val_loss: 0.4438 - val_accuracy: 0.9040\n",
            "Epoch 312/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0223 - accuracy: 0.9962 - val_loss: 0.5435 - val_accuracy: 0.8780\n",
            "Epoch 313/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0228 - accuracy: 0.9945 - val_loss: 0.3776 - val_accuracy: 0.9340\n",
            "Epoch 314/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0243 - accuracy: 0.9935 - val_loss: 0.3955 - val_accuracy: 0.9090\n",
            "Epoch 315/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0226 - accuracy: 0.9957 - val_loss: 0.2315 - val_accuracy: 0.9430\n",
            "Epoch 316/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0217 - accuracy: 0.9952 - val_loss: 0.2867 - val_accuracy: 0.9310\n",
            "Epoch 317/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0212 - accuracy: 0.9960 - val_loss: 0.3317 - val_accuracy: 0.9240\n",
            "Epoch 318/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0225 - accuracy: 0.9955 - val_loss: 0.5849 - val_accuracy: 0.9100\n",
            "Epoch 319/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0205 - accuracy: 0.9960 - val_loss: 0.3488 - val_accuracy: 0.9380\n",
            "Epoch 320/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0222 - accuracy: 0.9955 - val_loss: 0.2588 - val_accuracy: 0.9460\n",
            "Epoch 321/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0194 - accuracy: 0.9958 - val_loss: 1.2511 - val_accuracy: 0.8080\n",
            "Epoch 322/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0215 - accuracy: 0.9952 - val_loss: 0.2727 - val_accuracy: 0.9270\n",
            "Epoch 323/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0210 - accuracy: 0.9948 - val_loss: 0.1991 - val_accuracy: 0.9470\n",
            "Epoch 324/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0194 - accuracy: 0.9957 - val_loss: 0.2332 - val_accuracy: 0.9420\n",
            "Epoch 325/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0196 - accuracy: 0.9957 - val_loss: 0.1445 - val_accuracy: 0.9590\n",
            "Epoch 326/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0202 - accuracy: 0.9952 - val_loss: 0.6648 - val_accuracy: 0.8840\n",
            "Epoch 327/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0221 - accuracy: 0.9947 - val_loss: 0.3410 - val_accuracy: 0.9230\n",
            "Epoch 328/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0176 - accuracy: 0.9967 - val_loss: 0.2612 - val_accuracy: 0.9460\n",
            "Epoch 329/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0207 - accuracy: 0.9953 - val_loss: 0.9092 - val_accuracy: 0.8330\n",
            "Epoch 330/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0182 - accuracy: 0.9968 - val_loss: 0.1351 - val_accuracy: 0.9640\n",
            "Epoch 331/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0162 - accuracy: 0.9970 - val_loss: 0.1482 - val_accuracy: 0.9610\n",
            "Epoch 332/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0250 - accuracy: 0.9952 - val_loss: 0.1777 - val_accuracy: 0.9490\n",
            "Epoch 333/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0270 - accuracy: 0.9940 - val_loss: 0.1727 - val_accuracy: 0.9580\n",
            "Epoch 334/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0267 - accuracy: 0.9937 - val_loss: 0.2286 - val_accuracy: 0.9290\n",
            "Epoch 335/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0214 - accuracy: 0.9950 - val_loss: 0.1804 - val_accuracy: 0.9610\n",
            "Epoch 336/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0167 - accuracy: 0.9963 - val_loss: 1.5380 - val_accuracy: 0.7970\n",
            "Epoch 337/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0192 - accuracy: 0.9960 - val_loss: 0.8561 - val_accuracy: 0.8610\n",
            "Epoch 338/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0189 - accuracy: 0.9960 - val_loss: 1.1504 - val_accuracy: 0.8220\n",
            "Epoch 339/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0189 - accuracy: 0.9955 - val_loss: 0.4669 - val_accuracy: 0.8980\n",
            "Epoch 340/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0177 - accuracy: 0.9955 - val_loss: 0.3850 - val_accuracy: 0.9300\n",
            "Epoch 341/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0176 - accuracy: 0.9963 - val_loss: 0.1707 - val_accuracy: 0.9540\n",
            "Epoch 342/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0148 - accuracy: 0.9972 - val_loss: 0.1753 - val_accuracy: 0.9520\n",
            "Epoch 343/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0166 - accuracy: 0.9965 - val_loss: 0.5208 - val_accuracy: 0.8910\n",
            "Epoch 344/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0200 - accuracy: 0.9950 - val_loss: 0.6180 - val_accuracy: 0.8740\n",
            "Epoch 345/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0146 - accuracy: 0.9972 - val_loss: 0.3454 - val_accuracy: 0.9330\n",
            "Epoch 346/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0164 - accuracy: 0.9963 - val_loss: 0.2912 - val_accuracy: 0.9330\n",
            "Epoch 347/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0154 - accuracy: 0.9962 - val_loss: 0.2240 - val_accuracy: 0.9480\n",
            "Epoch 348/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0153 - accuracy: 0.9968 - val_loss: 0.9263 - val_accuracy: 0.8550\n",
            "Epoch 349/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0318 - accuracy: 0.9933 - val_loss: 0.2715 - val_accuracy: 0.9210\n",
            "Epoch 350/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0233 - accuracy: 0.9953 - val_loss: 0.2371 - val_accuracy: 0.9410\n",
            "Epoch 351/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0174 - accuracy: 0.9968 - val_loss: 0.2566 - val_accuracy: 0.9430\n",
            "Epoch 352/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0155 - accuracy: 0.9958 - val_loss: 0.1802 - val_accuracy: 0.9540\n",
            "Epoch 353/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0159 - accuracy: 0.9962 - val_loss: 0.6074 - val_accuracy: 0.8900\n",
            "Epoch 354/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0181 - accuracy: 0.9960 - val_loss: 0.6376 - val_accuracy: 0.8670\n",
            "Epoch 355/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0156 - accuracy: 0.9958 - val_loss: 0.4616 - val_accuracy: 0.9220\n",
            "Epoch 356/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0147 - accuracy: 0.9972 - val_loss: 0.1732 - val_accuracy: 0.9600\n",
            "Epoch 357/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0205 - accuracy: 0.9947 - val_loss: 0.3166 - val_accuracy: 0.9220\n",
            "Epoch 358/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0199 - accuracy: 0.9955 - val_loss: 0.3424 - val_accuracy: 0.9130\n",
            "Epoch 359/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0181 - accuracy: 0.9957 - val_loss: 0.4749 - val_accuracy: 0.9140\n",
            "Epoch 360/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0165 - accuracy: 0.9967 - val_loss: 0.1275 - val_accuracy: 0.9650\n",
            "Epoch 361/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0128 - accuracy: 0.9975 - val_loss: 0.1660 - val_accuracy: 0.9460\n",
            "Epoch 362/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0162 - accuracy: 0.9958 - val_loss: 0.2165 - val_accuracy: 0.9510\n",
            "Epoch 363/400\n",
            "188/188 [==============================] - 14s 74ms/step - loss: 0.0162 - accuracy: 0.9960 - val_loss: 0.2693 - val_accuracy: 0.9370\n",
            "Epoch 364/400\n",
            "188/188 [==============================] - 13s 70ms/step - loss: 0.0160 - accuracy: 0.9967 - val_loss: 0.1692 - val_accuracy: 0.9530\n",
            "Epoch 365/400\n",
            "188/188 [==============================] - 13s 71ms/step - loss: 0.0125 - accuracy: 0.9982 - val_loss: 0.1812 - val_accuracy: 0.9580\n",
            "Epoch 366/400\n",
            "188/188 [==============================] - 13s 70ms/step - loss: 0.0155 - accuracy: 0.9965 - val_loss: 0.2800 - val_accuracy: 0.9310\n",
            "Epoch 367/400\n",
            "188/188 [==============================] - 13s 71ms/step - loss: 0.0162 - accuracy: 0.9965 - val_loss: 0.9414 - val_accuracy: 0.8810\n",
            "Epoch 368/400\n",
            "188/188 [==============================] - 13s 70ms/step - loss: 0.0165 - accuracy: 0.9967 - val_loss: 0.4556 - val_accuracy: 0.9270\n",
            "Epoch 369/400\n",
            "188/188 [==============================] - 13s 70ms/step - loss: 0.0147 - accuracy: 0.9963 - val_loss: 0.4640 - val_accuracy: 0.8910\n",
            "Epoch 370/400\n",
            "188/188 [==============================] - 13s 69ms/step - loss: 0.0169 - accuracy: 0.9963 - val_loss: 0.9272 - val_accuracy: 0.8700\n",
            "Epoch 371/400\n",
            "188/188 [==============================] - 13s 69ms/step - loss: 0.0160 - accuracy: 0.9957 - val_loss: 0.2678 - val_accuracy: 0.9210\n",
            "Epoch 372/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0154 - accuracy: 0.9965 - val_loss: 1.5515 - val_accuracy: 0.7700\n",
            "Epoch 373/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0166 - accuracy: 0.9958 - val_loss: 0.1672 - val_accuracy: 0.9520\n",
            "Epoch 374/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0172 - accuracy: 0.9948 - val_loss: 0.3125 - val_accuracy: 0.9410\n",
            "Epoch 375/400\n",
            "188/188 [==============================] - 14s 77ms/step - loss: 0.0163 - accuracy: 0.9968 - val_loss: 0.1962 - val_accuracy: 0.9550\n",
            "Epoch 376/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0122 - accuracy: 0.9975 - val_loss: 0.1267 - val_accuracy: 0.9660\n",
            "Epoch 377/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0168 - accuracy: 0.9960 - val_loss: 0.3843 - val_accuracy: 0.9280\n",
            "Epoch 378/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0140 - accuracy: 0.9968 - val_loss: 0.2900 - val_accuracy: 0.9470\n",
            "Epoch 379/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0153 - accuracy: 0.9958 - val_loss: 0.1687 - val_accuracy: 0.9590\n",
            "Epoch 380/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0149 - accuracy: 0.9965 - val_loss: 0.1761 - val_accuracy: 0.9500\n",
            "Epoch 381/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0168 - accuracy: 0.9955 - val_loss: 0.1499 - val_accuracy: 0.9580\n",
            "Epoch 382/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0124 - accuracy: 0.9978 - val_loss: 0.2140 - val_accuracy: 0.9510\n",
            "Epoch 383/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0112 - accuracy: 0.9980 - val_loss: 0.1518 - val_accuracy: 0.9610\n",
            "Epoch 384/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 0.1642 - val_accuracy: 0.9560\n",
            "Epoch 385/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0154 - accuracy: 0.9958 - val_loss: 0.3484 - val_accuracy: 0.9240\n",
            "Epoch 386/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0125 - accuracy: 0.9975 - val_loss: 0.4506 - val_accuracy: 0.9080\n",
            "Epoch 387/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0192 - accuracy: 0.9965 - val_loss: 1.3866 - val_accuracy: 0.7980\n",
            "Epoch 388/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0180 - accuracy: 0.9967 - val_loss: 0.2963 - val_accuracy: 0.9290\n",
            "Epoch 389/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0160 - accuracy: 0.9963 - val_loss: 0.5510 - val_accuracy: 0.8820\n",
            "Epoch 390/400\n",
            "188/188 [==============================] - 14s 75ms/step - loss: 0.0129 - accuracy: 0.9972 - val_loss: 0.2765 - val_accuracy: 0.9300\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(Gamma_container, intLabels10, validation_data=(Gamma_container_val, intValLabels10),  epochs=400, callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AU5qT-5Iu6VI",
        "outputId": "fe6667d7-32da-4f9e-b03a-90342442d3b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1994b157730>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "plt.subplot(1,2,1)\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Model for resolution 30°"
      ],
      "metadata": {
        "id": "5oiyUjyD3AjD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Network architecture with K = 12 output classes"
      ],
      "metadata": {
        "id": "FdKjnJEFUqOx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3HsIAm3xu6VI",
        "outputId": "323aaa01-b964-4eb0-9db8-3bc3a5cbbcfe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1 (Conv2D)               (None, 12, 509, 64)       5824      \n",
            "_________________________________________________________________\n",
            "bn1 (BatchNormalization)     (None, 12, 509, 64)       256       \n",
            "_________________________________________________________________\n",
            "conv2 (Conv2D)               (None, 10, 507, 64)       36928     \n",
            "_________________________________________________________________\n",
            "bn2 (BatchNormalization)     (None, 10, 507, 64)       256       \n",
            "_________________________________________________________________\n",
            "dn1 (Dropout)                (None, 10, 507, 64)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 324480)            0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 512)               166134272 \n",
            "_________________________________________________________________\n",
            "dn2 (Dropout)                (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dn3 (Dropout)                (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 12)                6156      \n",
            "=================================================================\n",
            "Total params: 166,446,348\n",
            "Trainable params: 166,446,092\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "filters = 64\n",
        "kernel_size = (3,3)\n",
        "strides = (1,1)\n",
        "input_shape = (14, 511, 10)\n",
        "rate = 0.5\n",
        "K = 12 \n",
        "\n",
        "model = keras.Sequential ([\n",
        "  # input layer (14 * 511 * 10) (convolutional layers + batch normalization (BN) w ReLU)\n",
        "  keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, input_shape=input_shape, activation='relu', name='conv1'),\n",
        "  keras.layers.BatchNormalization(name = 'bn1'),\n",
        "\n",
        "  # 2nd convolutional layers + batch normalization (BN) w ReLU\n",
        "  keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, activation='relu', name='conv2'),\n",
        "  keras.layers.BatchNormalization(name = 'bn2'),\n",
        "  # dropout procedure with rate 0.5\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn1'),\n",
        "\n",
        "  # 1st fully connected layer w ReLU & dropout procedure with rate 0.5\n",
        "  keras.layers.Flatten(name='flatten'),\n",
        "  tf.keras.layers.Dense(512, activation = 'relu', name = 'fc1'),\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn2'),\n",
        "\n",
        "  # 2nd fully connected layer w ReLU & dropout procedure with rate 0.5\n",
        "  tf.keras.layers.Dense(512, activation = 'relu', name = 'fc2'),\n",
        "  tf.keras.layers.Dropout(rate, name = 'dn3'),\n",
        "\n",
        "  # SoftMax\n",
        "  tf.keras.layers.Dense(K, activation = 'softmax', name = 'output'),\n",
        "])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tHNjofC1u6VI"
      },
      "outputs": [],
      "source": [
        "# Compile\n",
        "learning_rate = 1e-5 \n",
        "optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3B0Kg_Epu6VJ"
      },
      "outputs": [],
      "source": [
        "# Callbacks\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath='best_model30',\n",
        "    save_weights_only=True,\n",
        "    monitor='val_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True)\n",
        "\n",
        "\n",
        "callbacks = [\n",
        "  model_checkpoint_callback,\n",
        "  tf.keras.callbacks.EarlyStopping(patience=100, monitor='accuracy', mode='max'),\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xI2LzWGSu6VJ"
      },
      "outputs": [],
      "source": [
        "# Shuffling features\n",
        "(Gamma_container, intLabels30) = shuffle(Gamma_container, intLabels30)\n",
        "(Gamma_container_val, intValLabels30) = shuffle(Gamma_container_val, intValLabels30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWhV6_luu6VJ",
        "outputId": "cf17a7ee-3203-4b49-ee50-225d27111f17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "188/188 [==============================] - 25s 108ms/step - loss: 2.2880 - accuracy: 0.3123 - val_loss: 2.4830 - val_accuracy: 0.0840\n",
            "Epoch 2/400\n",
            "188/188 [==============================] - 17s 93ms/step - loss: 1.5516 - accuracy: 0.4523 - val_loss: 2.4764 - val_accuracy: 0.0945\n",
            "Epoch 3/400\n",
            "188/188 [==============================] - 17s 89ms/step - loss: 1.1743 - accuracy: 0.5515 - val_loss: 2.6756 - val_accuracy: 0.0842\n",
            "Epoch 4/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 1.0156 - accuracy: 0.6098 - val_loss: 4.7218 - val_accuracy: 0.1042\n",
            "Epoch 5/400\n",
            "188/188 [==============================] - 18s 92ms/step - loss: 0.9277 - accuracy: 0.6475 - val_loss: 4.1365 - val_accuracy: 0.1703\n",
            "Epoch 6/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.8276 - accuracy: 0.6872 - val_loss: 10.0541 - val_accuracy: 0.1565\n",
            "Epoch 7/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.7617 - accuracy: 0.7175 - val_loss: 4.5444 - val_accuracy: 0.2173\n",
            "Epoch 8/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.7132 - accuracy: 0.7410 - val_loss: 10.2704 - val_accuracy: 0.1633\n",
            "Epoch 9/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.6814 - accuracy: 0.7525 - val_loss: 7.3071 - val_accuracy: 0.1290\n",
            "Epoch 10/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.6328 - accuracy: 0.7787 - val_loss: 4.9881 - val_accuracy: 0.2180\n",
            "Epoch 11/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.6036 - accuracy: 0.7860 - val_loss: 3.3060 - val_accuracy: 0.3058\n",
            "Epoch 12/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.5553 - accuracy: 0.8027 - val_loss: 2.2292 - val_accuracy: 0.2558\n",
            "Epoch 13/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.5347 - accuracy: 0.8117 - val_loss: 1.5077 - val_accuracy: 0.4868\n",
            "Epoch 14/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.5064 - accuracy: 0.8242 - val_loss: 1.6182 - val_accuracy: 0.3237\n",
            "Epoch 15/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.4840 - accuracy: 0.8367 - val_loss: 2.0341 - val_accuracy: 0.3898\n",
            "Epoch 16/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.4714 - accuracy: 0.8377 - val_loss: 2.8464 - val_accuracy: 0.3392\n",
            "Epoch 17/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.4278 - accuracy: 0.8572 - val_loss: 0.5505 - val_accuracy: 0.7550\n",
            "Epoch 18/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.4161 - accuracy: 0.8630 - val_loss: 0.5776 - val_accuracy: 0.7735\n",
            "Epoch 19/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3964 - accuracy: 0.8642 - val_loss: 0.7148 - val_accuracy: 0.7002\n",
            "Epoch 20/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3913 - accuracy: 0.8725 - val_loss: 2.1313 - val_accuracy: 0.3867\n",
            "Epoch 21/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3663 - accuracy: 0.8792 - val_loss: 1.2844 - val_accuracy: 0.5507\n",
            "Epoch 22/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3478 - accuracy: 0.8847 - val_loss: 0.3091 - val_accuracy: 0.8993\n",
            "Epoch 23/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3391 - accuracy: 0.8875 - val_loss: 1.1411 - val_accuracy: 0.5545\n",
            "Epoch 24/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3178 - accuracy: 0.9025 - val_loss: 0.5448 - val_accuracy: 0.7770\n",
            "Epoch 25/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.3013 - accuracy: 0.9015 - val_loss: 0.6298 - val_accuracy: 0.7825\n",
            "Epoch 26/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.2993 - accuracy: 0.9068 - val_loss: 0.4267 - val_accuracy: 0.8352\n",
            "Epoch 27/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.2805 - accuracy: 0.9102 - val_loss: 0.6070 - val_accuracy: 0.7850\n",
            "Epoch 28/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.2840 - accuracy: 0.9092 - val_loss: 0.6262 - val_accuracy: 0.7743\n",
            "Epoch 29/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.2573 - accuracy: 0.9207 - val_loss: 0.5936 - val_accuracy: 0.8273\n",
            "Epoch 30/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.2425 - accuracy: 0.9292 - val_loss: 0.2490 - val_accuracy: 0.9123\n",
            "Epoch 31/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2361 - accuracy: 0.9248 - val_loss: 0.5540 - val_accuracy: 0.8018\n",
            "Epoch 32/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2341 - accuracy: 0.9260 - val_loss: 0.6799 - val_accuracy: 0.7147\n",
            "Epoch 33/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2240 - accuracy: 0.9317 - val_loss: 0.4797 - val_accuracy: 0.8618\n",
            "Epoch 34/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2204 - accuracy: 0.9333 - val_loss: 1.8312 - val_accuracy: 0.4665\n",
            "Epoch 35/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2101 - accuracy: 0.9308 - val_loss: 0.5127 - val_accuracy: 0.7920\n",
            "Epoch 36/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.2066 - accuracy: 0.9365 - val_loss: 0.1102 - val_accuracy: 0.9735\n",
            "Epoch 37/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1834 - accuracy: 0.9452 - val_loss: 0.5872 - val_accuracy: 0.7865\n",
            "Epoch 38/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1798 - accuracy: 0.9457 - val_loss: 0.8659 - val_accuracy: 0.6578\n",
            "Epoch 39/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1796 - accuracy: 0.9457 - val_loss: 1.1141 - val_accuracy: 0.5128\n",
            "Epoch 40/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1793 - accuracy: 0.9453 - val_loss: 0.3349 - val_accuracy: 0.8778\n",
            "Epoch 41/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1702 - accuracy: 0.9483 - val_loss: 0.5408 - val_accuracy: 0.7855\n",
            "Epoch 42/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1693 - accuracy: 0.9543 - val_loss: 0.5745 - val_accuracy: 0.7923\n",
            "Epoch 43/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1602 - accuracy: 0.9520 - val_loss: 1.1824 - val_accuracy: 0.5733\n",
            "Epoch 44/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.1537 - accuracy: 0.9552 - val_loss: 0.1350 - val_accuracy: 0.9550\n",
            "Epoch 45/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1505 - accuracy: 0.9565 - val_loss: 0.7371 - val_accuracy: 0.7863\n",
            "Epoch 46/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1559 - accuracy: 0.9532 - val_loss: 0.3954 - val_accuracy: 0.8698\n",
            "Epoch 47/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1456 - accuracy: 0.9603 - val_loss: 0.3739 - val_accuracy: 0.9237\n",
            "Epoch 48/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1441 - accuracy: 0.9577 - val_loss: 0.4847 - val_accuracy: 0.8233\n",
            "Epoch 49/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1390 - accuracy: 0.9597 - val_loss: 0.2720 - val_accuracy: 0.9088\n",
            "Epoch 50/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1270 - accuracy: 0.9617 - val_loss: 0.9502 - val_accuracy: 0.6497\n",
            "Epoch 51/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1312 - accuracy: 0.9632 - val_loss: 1.0470 - val_accuracy: 0.5948\n",
            "Epoch 52/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1267 - accuracy: 0.9638 - val_loss: 0.3057 - val_accuracy: 0.9255\n",
            "Epoch 53/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1172 - accuracy: 0.9655 - val_loss: 0.3427 - val_accuracy: 0.8685\n",
            "Epoch 54/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1240 - accuracy: 0.9632 - val_loss: 0.3676 - val_accuracy: 0.8602\n",
            "Epoch 55/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1124 - accuracy: 0.9690 - val_loss: 0.2364 - val_accuracy: 0.9242\n",
            "Epoch 56/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1202 - accuracy: 0.9662 - val_loss: 0.1391 - val_accuracy: 0.9528\n",
            "Epoch 57/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1110 - accuracy: 0.9673 - val_loss: 0.2235 - val_accuracy: 0.9228\n",
            "Epoch 58/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1045 - accuracy: 0.9732 - val_loss: 0.2429 - val_accuracy: 0.9273\n",
            "Epoch 59/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1097 - accuracy: 0.9682 - val_loss: 0.7253 - val_accuracy: 0.7730\n",
            "Epoch 60/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.1156 - accuracy: 0.9663 - val_loss: 0.3693 - val_accuracy: 0.8633\n",
            "Epoch 61/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.1028 - accuracy: 0.9703 - val_loss: 0.1391 - val_accuracy: 0.9603\n",
            "Epoch 62/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.1061 - accuracy: 0.9705 - val_loss: 0.2728 - val_accuracy: 0.9278\n",
            "Epoch 63/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.1046 - accuracy: 0.9732 - val_loss: 0.1858 - val_accuracy: 0.9375\n",
            "Epoch 64/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0939 - accuracy: 0.9727 - val_loss: 0.1442 - val_accuracy: 0.9522\n",
            "Epoch 65/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0905 - accuracy: 0.9743 - val_loss: 0.1348 - val_accuracy: 0.9623\n",
            "Epoch 66/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0927 - accuracy: 0.9732 - val_loss: 0.3495 - val_accuracy: 0.9258\n",
            "Epoch 67/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0988 - accuracy: 0.9727 - val_loss: 0.3527 - val_accuracy: 0.8877\n",
            "Epoch 68/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0925 - accuracy: 0.9745 - val_loss: 0.1831 - val_accuracy: 0.9490\n",
            "Epoch 69/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0938 - accuracy: 0.9757 - val_loss: 0.2645 - val_accuracy: 0.9152\n",
            "Epoch 70/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0920 - accuracy: 0.9743 - val_loss: 0.6502 - val_accuracy: 0.7967\n",
            "Epoch 71/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0864 - accuracy: 0.9750 - val_loss: 0.1391 - val_accuracy: 0.9507\n",
            "Epoch 72/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0826 - accuracy: 0.9802 - val_loss: 0.0728 - val_accuracy: 0.9803\n",
            "Epoch 73/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0816 - accuracy: 0.9778 - val_loss: 0.1961 - val_accuracy: 0.9408\n",
            "Epoch 74/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0837 - accuracy: 0.9767 - val_loss: 0.3108 - val_accuracy: 0.8882\n",
            "Epoch 75/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0809 - accuracy: 0.9763 - val_loss: 0.3320 - val_accuracy: 0.8705\n",
            "Epoch 76/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0813 - accuracy: 0.9775 - val_loss: 0.1284 - val_accuracy: 0.9603\n",
            "Epoch 77/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0850 - accuracy: 0.9767 - val_loss: 1.8974 - val_accuracy: 0.4103\n",
            "Epoch 78/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0768 - accuracy: 0.9807 - val_loss: 0.8631 - val_accuracy: 0.6885\n",
            "Epoch 79/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0779 - accuracy: 0.9793 - val_loss: 0.4344 - val_accuracy: 0.8312\n",
            "Epoch 80/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0772 - accuracy: 0.9798 - val_loss: 0.2106 - val_accuracy: 0.9237\n",
            "Epoch 81/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0768 - accuracy: 0.9788 - val_loss: 0.2115 - val_accuracy: 0.9323\n",
            "Epoch 82/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0730 - accuracy: 0.9815 - val_loss: 0.0695 - val_accuracy: 0.9797\n",
            "Epoch 83/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0721 - accuracy: 0.9807 - val_loss: 0.2754 - val_accuracy: 0.9098\n",
            "Epoch 84/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0679 - accuracy: 0.9823 - val_loss: 0.2369 - val_accuracy: 0.9222\n",
            "Epoch 85/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0690 - accuracy: 0.9813 - val_loss: 0.5621 - val_accuracy: 0.7955\n",
            "Epoch 86/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0692 - accuracy: 0.9810 - val_loss: 0.4863 - val_accuracy: 0.8565\n",
            "Epoch 87/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0700 - accuracy: 0.9803 - val_loss: 0.1713 - val_accuracy: 0.9432\n",
            "Epoch 88/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0689 - accuracy: 0.9802 - val_loss: 0.1304 - val_accuracy: 0.9553\n",
            "Epoch 89/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0684 - accuracy: 0.9828 - val_loss: 0.6177 - val_accuracy: 0.7695\n",
            "Epoch 90/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0636 - accuracy: 0.9822 - val_loss: 0.6687 - val_accuracy: 0.7490\n",
            "Epoch 91/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0677 - accuracy: 0.9797 - val_loss: 0.1017 - val_accuracy: 0.9635\n",
            "Epoch 92/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0652 - accuracy: 0.9822 - val_loss: 0.4153 - val_accuracy: 0.8570\n",
            "Epoch 93/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0630 - accuracy: 0.9823 - val_loss: 0.5266 - val_accuracy: 0.8527\n",
            "Epoch 94/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0612 - accuracy: 0.9838 - val_loss: 0.1499 - val_accuracy: 0.9483\n",
            "Epoch 95/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0613 - accuracy: 0.9845 - val_loss: 0.1330 - val_accuracy: 0.9550\n",
            "Epoch 96/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0567 - accuracy: 0.9835 - val_loss: 0.4584 - val_accuracy: 0.8435\n",
            "Epoch 97/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0586 - accuracy: 0.9847 - val_loss: 0.3202 - val_accuracy: 0.8875\n",
            "Epoch 98/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.0552 - accuracy: 0.9880 - val_loss: 0.3735 - val_accuracy: 0.8728\n",
            "Epoch 99/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0577 - accuracy: 0.9840 - val_loss: 0.0559 - val_accuracy: 0.9798\n",
            "Epoch 100/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0554 - accuracy: 0.9835 - val_loss: 0.0567 - val_accuracy: 0.9780\n",
            "Epoch 101/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0555 - accuracy: 0.9840 - val_loss: 0.0649 - val_accuracy: 0.9788\n",
            "Epoch 102/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0557 - accuracy: 0.9883 - val_loss: 0.5438 - val_accuracy: 0.7825\n",
            "Epoch 103/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0576 - accuracy: 0.9858 - val_loss: 0.7178 - val_accuracy: 0.7895\n",
            "Epoch 104/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0589 - accuracy: 0.9827 - val_loss: 0.1143 - val_accuracy: 0.9598\n",
            "Epoch 105/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0527 - accuracy: 0.9867 - val_loss: 0.0669 - val_accuracy: 0.9780\n",
            "Epoch 106/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0540 - accuracy: 0.9850 - val_loss: 0.4411 - val_accuracy: 0.8267\n",
            "Epoch 107/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0536 - accuracy: 0.9837 - val_loss: 0.0716 - val_accuracy: 0.9775\n",
            "Epoch 108/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0510 - accuracy: 0.9892 - val_loss: 0.1160 - val_accuracy: 0.9730\n",
            "Epoch 109/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0516 - accuracy: 0.9867 - val_loss: 0.4096 - val_accuracy: 0.8618\n",
            "Epoch 110/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0525 - accuracy: 0.9878 - val_loss: 0.1488 - val_accuracy: 0.9507\n",
            "Epoch 111/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0528 - accuracy: 0.9852 - val_loss: 0.1633 - val_accuracy: 0.9387\n",
            "Epoch 112/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0513 - accuracy: 0.9882 - val_loss: 0.0971 - val_accuracy: 0.9643\n",
            "Epoch 113/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0543 - accuracy: 0.9862 - val_loss: 0.0511 - val_accuracy: 0.9815\n",
            "Epoch 114/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0504 - accuracy: 0.9858 - val_loss: 0.5680 - val_accuracy: 0.7808\n",
            "Epoch 115/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0480 - accuracy: 0.9873 - val_loss: 0.9346 - val_accuracy: 0.6680\n",
            "Epoch 116/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0493 - accuracy: 0.9873 - val_loss: 0.1018 - val_accuracy: 0.9623\n",
            "Epoch 117/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0502 - accuracy: 0.9878 - val_loss: 0.4868 - val_accuracy: 0.8335\n",
            "Epoch 118/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0471 - accuracy: 0.9887 - val_loss: 0.0889 - val_accuracy: 0.9725\n",
            "Epoch 119/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0501 - accuracy: 0.9863 - val_loss: 0.3991 - val_accuracy: 0.8453\n",
            "Epoch 120/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0470 - accuracy: 0.9867 - val_loss: 0.1948 - val_accuracy: 0.9403\n",
            "Epoch 121/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0476 - accuracy: 0.9888 - val_loss: 0.4988 - val_accuracy: 0.8763\n",
            "Epoch 122/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0482 - accuracy: 0.9887 - val_loss: 0.5988 - val_accuracy: 0.8062\n",
            "Epoch 123/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0446 - accuracy: 0.9888 - val_loss: 0.0848 - val_accuracy: 0.9745\n",
            "Epoch 124/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0455 - accuracy: 0.9887 - val_loss: 0.2498 - val_accuracy: 0.9060\n",
            "Epoch 125/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0461 - accuracy: 0.9888 - val_loss: 0.1296 - val_accuracy: 0.9648\n",
            "Epoch 126/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0448 - accuracy: 0.9888 - val_loss: 0.0659 - val_accuracy: 0.9803\n",
            "Epoch 127/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0431 - accuracy: 0.9883 - val_loss: 0.1828 - val_accuracy: 0.9418\n",
            "Epoch 128/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0443 - accuracy: 0.9890 - val_loss: 0.2221 - val_accuracy: 0.9118\n",
            "Epoch 129/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0426 - accuracy: 0.9907 - val_loss: 0.8591 - val_accuracy: 0.7270\n",
            "Epoch 130/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0411 - accuracy: 0.9890 - val_loss: 0.0544 - val_accuracy: 0.9777\n",
            "Epoch 131/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0383 - accuracy: 0.9898 - val_loss: 0.1191 - val_accuracy: 0.9650\n",
            "Epoch 132/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0405 - accuracy: 0.9890 - val_loss: 0.1367 - val_accuracy: 0.9555\n",
            "Epoch 133/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0426 - accuracy: 0.9885 - val_loss: 0.2757 - val_accuracy: 0.9025\n",
            "Epoch 134/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0373 - accuracy: 0.9918 - val_loss: 0.1108 - val_accuracy: 0.9567\n",
            "Epoch 135/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0386 - accuracy: 0.9905 - val_loss: 0.1460 - val_accuracy: 0.9503\n",
            "Epoch 136/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0401 - accuracy: 0.9900 - val_loss: 0.5422 - val_accuracy: 0.8280\n",
            "Epoch 137/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0395 - accuracy: 0.9897 - val_loss: 0.1966 - val_accuracy: 0.9270\n",
            "Epoch 138/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0364 - accuracy: 0.9905 - val_loss: 0.3426 - val_accuracy: 0.8822\n",
            "Epoch 139/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0400 - accuracy: 0.9905 - val_loss: 0.1515 - val_accuracy: 0.9423\n",
            "Epoch 140/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.1092 - val_accuracy: 0.9622\n",
            "Epoch 141/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0384 - accuracy: 0.9900 - val_loss: 0.1404 - val_accuracy: 0.9523\n",
            "Epoch 142/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0416 - accuracy: 0.9875 - val_loss: 0.2665 - val_accuracy: 0.9088\n",
            "Epoch 143/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0387 - accuracy: 0.9902 - val_loss: 0.1572 - val_accuracy: 0.9505\n",
            "Epoch 144/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0436 - accuracy: 0.9883 - val_loss: 0.1234 - val_accuracy: 0.9618\n",
            "Epoch 145/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0361 - accuracy: 0.9913 - val_loss: 0.4963 - val_accuracy: 0.8737\n",
            "Epoch 146/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0403 - accuracy: 0.9892 - val_loss: 0.5500 - val_accuracy: 0.8375\n",
            "Epoch 147/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0387 - accuracy: 0.9918 - val_loss: 0.1134 - val_accuracy: 0.9605\n",
            "Epoch 148/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0375 - accuracy: 0.9912 - val_loss: 0.0837 - val_accuracy: 0.9687\n",
            "Epoch 149/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0355 - accuracy: 0.9922 - val_loss: 0.0968 - val_accuracy: 0.9667\n",
            "Epoch 150/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0371 - accuracy: 0.9907 - val_loss: 0.4226 - val_accuracy: 0.8500\n",
            "Epoch 151/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0386 - accuracy: 0.9887 - val_loss: 0.4344 - val_accuracy: 0.8720\n",
            "Epoch 152/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0373 - accuracy: 0.9917 - val_loss: 0.0457 - val_accuracy: 0.9837\n",
            "Epoch 153/400\n",
            "188/188 [==============================] - 18s 92ms/step - loss: 0.0359 - accuracy: 0.9910 - val_loss: 0.4697 - val_accuracy: 0.8745\n",
            "Epoch 154/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0347 - accuracy: 0.9910 - val_loss: 0.0821 - val_accuracy: 0.9670\n",
            "Epoch 155/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0340 - accuracy: 0.9915 - val_loss: 0.4821 - val_accuracy: 0.8647\n",
            "Epoch 156/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0382 - accuracy: 0.9892 - val_loss: 0.1376 - val_accuracy: 0.9553\n",
            "Epoch 157/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0360 - accuracy: 0.9902 - val_loss: 0.5832 - val_accuracy: 0.8165\n",
            "Epoch 158/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0356 - accuracy: 0.9913 - val_loss: 0.2228 - val_accuracy: 0.9247\n",
            "Epoch 159/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0351 - accuracy: 0.9912 - val_loss: 0.3619 - val_accuracy: 0.8852\n",
            "Epoch 160/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0391 - accuracy: 0.9888 - val_loss: 0.1653 - val_accuracy: 0.9397\n",
            "Epoch 161/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0355 - accuracy: 0.9908 - val_loss: 0.6413 - val_accuracy: 0.8045\n",
            "Epoch 162/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 0.0635 - val_accuracy: 0.9760\n",
            "Epoch 163/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0334 - accuracy: 0.9918 - val_loss: 0.0999 - val_accuracy: 0.9653\n",
            "Epoch 164/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0350 - accuracy: 0.9907 - val_loss: 0.3761 - val_accuracy: 0.8750\n",
            "Epoch 165/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0352 - accuracy: 0.9927 - val_loss: 0.1963 - val_accuracy: 0.9353\n",
            "Epoch 166/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0367 - accuracy: 0.9913 - val_loss: 0.1904 - val_accuracy: 0.9360\n",
            "Epoch 167/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0348 - accuracy: 0.9898 - val_loss: 0.2175 - val_accuracy: 0.9170\n",
            "Epoch 168/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0339 - accuracy: 0.9915 - val_loss: 0.2148 - val_accuracy: 0.9373\n",
            "Epoch 169/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0313 - accuracy: 0.9930 - val_loss: 0.0723 - val_accuracy: 0.9753\n",
            "Epoch 170/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0324 - accuracy: 0.9922 - val_loss: 0.7243 - val_accuracy: 0.7995\n",
            "Epoch 171/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0339 - accuracy: 0.9912 - val_loss: 0.4935 - val_accuracy: 0.8245\n",
            "Epoch 172/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0358 - accuracy: 0.9900 - val_loss: 0.6591 - val_accuracy: 0.8357\n",
            "Epoch 173/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0332 - accuracy: 0.9915 - val_loss: 1.7632 - val_accuracy: 0.6370\n",
            "Epoch 174/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0350 - accuracy: 0.9907 - val_loss: 0.5214 - val_accuracy: 0.8287\n",
            "Epoch 175/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0305 - accuracy: 0.9925 - val_loss: 0.0393 - val_accuracy: 0.9870\n",
            "Epoch 176/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0328 - accuracy: 0.9918 - val_loss: 0.2047 - val_accuracy: 0.9318\n",
            "Epoch 177/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0314 - accuracy: 0.9928 - val_loss: 0.1908 - val_accuracy: 0.9322\n",
            "Epoch 178/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0324 - accuracy: 0.9917 - val_loss: 0.0615 - val_accuracy: 0.9785\n",
            "Epoch 179/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0308 - accuracy: 0.9908 - val_loss: 0.1227 - val_accuracy: 0.9567\n",
            "Epoch 180/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0322 - accuracy: 0.9923 - val_loss: 0.1646 - val_accuracy: 0.9408\n",
            "Epoch 181/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.0317 - accuracy: 0.9922 - val_loss: 0.0762 - val_accuracy: 0.9735\n",
            "Epoch 182/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0296 - accuracy: 0.9935 - val_loss: 0.3231 - val_accuracy: 0.9020\n",
            "Epoch 183/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0310 - accuracy: 0.9927 - val_loss: 0.1068 - val_accuracy: 0.9632\n",
            "Epoch 184/400\n",
            "188/188 [==============================] - 17s 92ms/step - loss: 0.0303 - accuracy: 0.9923 - val_loss: 0.2515 - val_accuracy: 0.9137\n",
            "Epoch 185/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.0581 - val_accuracy: 0.9773\n",
            "Epoch 186/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0294 - accuracy: 0.9932 - val_loss: 0.6271 - val_accuracy: 0.8548\n",
            "Epoch 187/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0291 - accuracy: 0.9912 - val_loss: 1.1140 - val_accuracy: 0.7552\n",
            "Epoch 188/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0316 - accuracy: 0.9908 - val_loss: 3.2234 - val_accuracy: 0.5467\n",
            "Epoch 189/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0280 - accuracy: 0.9923 - val_loss: 0.2279 - val_accuracy: 0.9388\n",
            "Epoch 190/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0283 - accuracy: 0.9908 - val_loss: 0.0751 - val_accuracy: 0.9767\n",
            "Epoch 191/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0274 - accuracy: 0.9930 - val_loss: 0.8327 - val_accuracy: 0.7910\n",
            "Epoch 192/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0276 - accuracy: 0.9922 - val_loss: 0.1860 - val_accuracy: 0.9383\n",
            "Epoch 193/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0271 - accuracy: 0.9943 - val_loss: 0.0603 - val_accuracy: 0.9782\n",
            "Epoch 194/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0267 - accuracy: 0.9925 - val_loss: 0.6089 - val_accuracy: 0.8592\n",
            "Epoch 195/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0254 - accuracy: 0.9923 - val_loss: 0.0788 - val_accuracy: 0.9732\n",
            "Epoch 196/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0269 - accuracy: 0.9942 - val_loss: 0.2294 - val_accuracy: 0.9277\n",
            "Epoch 197/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0270 - accuracy: 0.9932 - val_loss: 0.2746 - val_accuracy: 0.8977\n",
            "Epoch 198/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0312 - accuracy: 0.9900 - val_loss: 1.5697 - val_accuracy: 0.7562\n",
            "Epoch 199/400\n",
            "188/188 [==============================] - 17s 90ms/step - loss: 0.0257 - accuracy: 0.9940 - val_loss: 0.1571 - val_accuracy: 0.9445\n",
            "Epoch 200/400\n",
            "188/188 [==============================] - 17s 88ms/step - loss: 0.0283 - accuracy: 0.9917 - val_loss: 0.1207 - val_accuracy: 0.9640\n",
            "Epoch 201/400\n",
            "188/188 [==============================] - 17s 88ms/step - loss: 0.0275 - accuracy: 0.9925 - val_loss: 1.0143 - val_accuracy: 0.7612\n",
            "Epoch 202/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0265 - accuracy: 0.9923 - val_loss: 0.0798 - val_accuracy: 0.9703\n",
            "Epoch 203/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0267 - accuracy: 0.9940 - val_loss: 0.0746 - val_accuracy: 0.9772\n",
            "Epoch 204/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0262 - accuracy: 0.9937 - val_loss: 0.2469 - val_accuracy: 0.9168\n",
            "Epoch 205/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0250 - accuracy: 0.9935 - val_loss: 0.1303 - val_accuracy: 0.9568\n",
            "Epoch 206/400\n",
            "188/188 [==============================] - 16s 86ms/step - loss: 0.0266 - accuracy: 0.9927 - val_loss: 0.1084 - val_accuracy: 0.9653\n",
            "Epoch 207/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0263 - accuracy: 0.9930 - val_loss: 0.1984 - val_accuracy: 0.9467\n",
            "Epoch 208/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0282 - accuracy: 0.9933 - val_loss: 0.4035 - val_accuracy: 0.8547\n",
            "Epoch 209/400\n",
            "188/188 [==============================] - 17s 88ms/step - loss: 0.0261 - accuracy: 0.9930 - val_loss: 5.1772 - val_accuracy: 0.4905\n",
            "Epoch 210/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0228 - accuracy: 0.9933 - val_loss: 0.0899 - val_accuracy: 0.9667\n",
            "Epoch 211/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0265 - accuracy: 0.9927 - val_loss: 0.1592 - val_accuracy: 0.9473\n",
            "Epoch 212/400\n",
            "188/188 [==============================] - 17s 89ms/step - loss: 0.0257 - accuracy: 0.9935 - val_loss: 0.4530 - val_accuracy: 0.8928\n",
            "Epoch 213/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0263 - accuracy: 0.9923 - val_loss: 0.6546 - val_accuracy: 0.8582\n",
            "Epoch 214/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0252 - accuracy: 0.9927 - val_loss: 0.3368 - val_accuracy: 0.8990\n",
            "Epoch 215/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0245 - accuracy: 0.9940 - val_loss: 0.0463 - val_accuracy: 0.9820\n",
            "Epoch 216/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0240 - accuracy: 0.9942 - val_loss: 0.1280 - val_accuracy: 0.9643\n",
            "Epoch 217/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0245 - accuracy: 0.9932 - val_loss: 0.0783 - val_accuracy: 0.9735\n",
            "Epoch 218/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0235 - accuracy: 0.9938 - val_loss: 1.7665 - val_accuracy: 0.6593\n",
            "Epoch 219/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0216 - accuracy: 0.9945 - val_loss: 0.0253 - val_accuracy: 0.9918\n",
            "Epoch 220/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0286 - accuracy: 0.9923 - val_loss: 0.1037 - val_accuracy: 0.9645\n",
            "Epoch 221/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0228 - accuracy: 0.9942 - val_loss: 0.0400 - val_accuracy: 0.9853\n",
            "Epoch 222/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0219 - accuracy: 0.9937 - val_loss: 0.2206 - val_accuracy: 0.9402\n",
            "Epoch 223/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0240 - accuracy: 0.9947 - val_loss: 0.1108 - val_accuracy: 0.9592\n",
            "Epoch 224/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0218 - accuracy: 0.9945 - val_loss: 0.9678 - val_accuracy: 0.8307\n",
            "Epoch 225/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0238 - accuracy: 0.9937 - val_loss: 0.0883 - val_accuracy: 0.9742\n",
            "Epoch 226/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0210 - accuracy: 0.9958 - val_loss: 0.0835 - val_accuracy: 0.9718\n",
            "Epoch 227/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0249 - accuracy: 0.9937 - val_loss: 0.0794 - val_accuracy: 0.9722\n",
            "Epoch 228/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0210 - accuracy: 0.9945 - val_loss: 0.0948 - val_accuracy: 0.9752\n",
            "Epoch 229/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0215 - accuracy: 0.9940 - val_loss: 0.0257 - val_accuracy: 0.9912\n",
            "Epoch 230/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0220 - accuracy: 0.9943 - val_loss: 0.2782 - val_accuracy: 0.9210\n",
            "Epoch 231/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0215 - accuracy: 0.9950 - val_loss: 0.0792 - val_accuracy: 0.9767\n",
            "Epoch 232/400\n",
            "188/188 [==============================] - 16s 86ms/step - loss: 0.0211 - accuracy: 0.9948 - val_loss: 0.7983 - val_accuracy: 0.8488\n",
            "Epoch 233/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0216 - accuracy: 0.9943 - val_loss: 1.3792 - val_accuracy: 0.7832\n",
            "Epoch 234/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0242 - accuracy: 0.9935 - val_loss: 0.3673 - val_accuracy: 0.9117\n",
            "Epoch 235/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0231 - accuracy: 0.9940 - val_loss: 0.3038 - val_accuracy: 0.9327\n",
            "Epoch 236/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0207 - accuracy: 0.9938 - val_loss: 0.0881 - val_accuracy: 0.9718\n",
            "Epoch 237/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0199 - accuracy: 0.9958 - val_loss: 0.0895 - val_accuracy: 0.9692\n",
            "Epoch 238/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0200 - accuracy: 0.9948 - val_loss: 0.0375 - val_accuracy: 0.9850\n",
            "Epoch 239/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0258 - accuracy: 0.9940 - val_loss: 0.1120 - val_accuracy: 0.9618\n",
            "Epoch 240/400\n",
            "188/188 [==============================] - 16s 86ms/step - loss: 0.0223 - accuracy: 0.9945 - val_loss: 0.8368 - val_accuracy: 0.8388\n",
            "Epoch 241/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0201 - accuracy: 0.9957 - val_loss: 0.2646 - val_accuracy: 0.9335\n",
            "Epoch 242/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0191 - accuracy: 0.9957 - val_loss: 0.0870 - val_accuracy: 0.9705\n",
            "Epoch 243/400\n",
            "188/188 [==============================] - 16s 87ms/step - loss: 0.0201 - accuracy: 0.9943 - val_loss: 0.1790 - val_accuracy: 0.9525\n",
            "Epoch 244/400\n",
            "188/188 [==============================] - 17s 91ms/step - loss: 0.0181 - accuracy: 0.9957 - val_loss: 0.0673 - val_accuracy: 0.9757\n",
            "Epoch 245/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0234 - accuracy: 0.9947 - val_loss: 0.9309 - val_accuracy: 0.8405\n",
            "Epoch 246/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0189 - accuracy: 0.9957 - val_loss: 0.0391 - val_accuracy: 0.9867\n",
            "Epoch 247/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0229 - accuracy: 0.9938 - val_loss: 7.2003 - val_accuracy: 0.5080\n",
            "Epoch 248/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0200 - accuracy: 0.9950 - val_loss: 0.2884 - val_accuracy: 0.9232\n",
            "Epoch 249/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0193 - accuracy: 0.9957 - val_loss: 0.0768 - val_accuracy: 0.9727\n",
            "Epoch 250/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0184 - accuracy: 0.9953 - val_loss: 0.3097 - val_accuracy: 0.9253\n",
            "Epoch 251/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0182 - accuracy: 0.9963 - val_loss: 0.2070 - val_accuracy: 0.9415\n",
            "Epoch 252/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0192 - accuracy: 0.9958 - val_loss: 0.2351 - val_accuracy: 0.9362\n",
            "Epoch 253/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0184 - accuracy: 0.9948 - val_loss: 1.4328 - val_accuracy: 0.7687\n",
            "Epoch 254/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0214 - accuracy: 0.9943 - val_loss: 0.2946 - val_accuracy: 0.9205\n",
            "Epoch 255/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0190 - accuracy: 0.9947 - val_loss: 0.0377 - val_accuracy: 0.9865\n",
            "Epoch 256/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0182 - accuracy: 0.9943 - val_loss: 1.8306 - val_accuracy: 0.6898\n",
            "Epoch 257/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0261 - accuracy: 0.9928 - val_loss: 0.3138 - val_accuracy: 0.9058\n",
            "Epoch 258/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0191 - accuracy: 0.9958 - val_loss: 1.0382 - val_accuracy: 0.8365\n",
            "Epoch 259/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0190 - accuracy: 0.9957 - val_loss: 0.1199 - val_accuracy: 0.9622\n",
            "Epoch 260/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0209 - accuracy: 0.9947 - val_loss: 0.4284 - val_accuracy: 0.8885\n",
            "Epoch 261/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0187 - accuracy: 0.9955 - val_loss: 0.5067 - val_accuracy: 0.9050\n",
            "Epoch 262/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0215 - accuracy: 0.9953 - val_loss: 0.1242 - val_accuracy: 0.9665\n",
            "Epoch 263/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0184 - accuracy: 0.9955 - val_loss: 0.1384 - val_accuracy: 0.9535\n",
            "Epoch 264/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0203 - accuracy: 0.9960 - val_loss: 1.0135 - val_accuracy: 0.8347\n",
            "Epoch 265/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0187 - accuracy: 0.9948 - val_loss: 0.2595 - val_accuracy: 0.9370\n",
            "Epoch 266/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0241 - accuracy: 0.9938 - val_loss: 0.6889 - val_accuracy: 0.8355\n",
            "Epoch 267/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0224 - accuracy: 0.9952 - val_loss: 0.0998 - val_accuracy: 0.9653\n",
            "Epoch 268/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0174 - accuracy: 0.9963 - val_loss: 0.0412 - val_accuracy: 0.9848\n",
            "Epoch 269/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0192 - accuracy: 0.9955 - val_loss: 0.2429 - val_accuracy: 0.9327\n",
            "Epoch 270/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0203 - accuracy: 0.9948 - val_loss: 0.2615 - val_accuracy: 0.9350\n",
            "Epoch 271/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0181 - accuracy: 0.9958 - val_loss: 0.1879 - val_accuracy: 0.9470\n",
            "Epoch 272/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0188 - accuracy: 0.9950 - val_loss: 0.0599 - val_accuracy: 0.9770\n",
            "Epoch 273/400\n",
            "188/188 [==============================] - 16s 85ms/step - loss: 0.0192 - accuracy: 0.9947 - val_loss: 0.7951 - val_accuracy: 0.8727\n",
            "Epoch 274/400\n",
            "188/188 [==============================] - 17s 88ms/step - loss: 0.0186 - accuracy: 0.9952 - val_loss: 0.2442 - val_accuracy: 0.9252\n",
            "Epoch 275/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0209 - accuracy: 0.9942 - val_loss: 0.0721 - val_accuracy: 0.9763\n",
            "Epoch 276/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0177 - accuracy: 0.9967 - val_loss: 0.2358 - val_accuracy: 0.9293\n",
            "Epoch 277/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0183 - accuracy: 0.9957 - val_loss: 0.2046 - val_accuracy: 0.9492\n",
            "Epoch 278/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0176 - accuracy: 0.9950 - val_loss: 4.7221 - val_accuracy: 0.5460\n",
            "Epoch 279/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0771 - val_accuracy: 0.9758\n",
            "Epoch 280/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0175 - accuracy: 0.9953 - val_loss: 0.0896 - val_accuracy: 0.9698\n",
            "Epoch 281/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0166 - accuracy: 0.9957 - val_loss: 2.0750 - val_accuracy: 0.7150\n",
            "Epoch 282/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0182 - accuracy: 0.9953 - val_loss: 0.3680 - val_accuracy: 0.9002\n",
            "Epoch 283/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0196 - accuracy: 0.9945 - val_loss: 0.8031 - val_accuracy: 0.8415\n",
            "Epoch 284/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0159 - accuracy: 0.9960 - val_loss: 0.4074 - val_accuracy: 0.9075\n",
            "Epoch 285/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0193 - accuracy: 0.9943 - val_loss: 0.1333 - val_accuracy: 0.9583\n",
            "Epoch 286/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0164 - accuracy: 0.9962 - val_loss: 0.0614 - val_accuracy: 0.9785\n",
            "Epoch 287/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0145 - accuracy: 0.9967 - val_loss: 0.6924 - val_accuracy: 0.8618\n",
            "Epoch 288/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0164 - accuracy: 0.9952 - val_loss: 0.2740 - val_accuracy: 0.9215\n",
            "Epoch 289/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0182 - accuracy: 0.9948 - val_loss: 0.9698 - val_accuracy: 0.8225\n",
            "Epoch 290/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0152 - accuracy: 0.9962 - val_loss: 0.1621 - val_accuracy: 0.9545\n",
            "Epoch 291/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0162 - accuracy: 0.9960 - val_loss: 0.8330 - val_accuracy: 0.8787\n",
            "Epoch 292/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0162 - accuracy: 0.9962 - val_loss: 0.0665 - val_accuracy: 0.9790\n",
            "Epoch 293/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0228 - accuracy: 0.9948 - val_loss: 1.0873 - val_accuracy: 0.8258\n",
            "Epoch 294/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0157 - accuracy: 0.9962 - val_loss: 0.3536 - val_accuracy: 0.9175\n",
            "Epoch 295/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0162 - accuracy: 0.9963 - val_loss: 0.0527 - val_accuracy: 0.9800\n",
            "Epoch 296/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0162 - accuracy: 0.9957 - val_loss: 0.9229 - val_accuracy: 0.8340\n",
            "Epoch 297/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0148 - accuracy: 0.9962 - val_loss: 0.1924 - val_accuracy: 0.9452\n",
            "Epoch 298/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0168 - accuracy: 0.9953 - val_loss: 0.4188 - val_accuracy: 0.9148\n",
            "Epoch 299/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0167 - accuracy: 0.9967 - val_loss: 0.0683 - val_accuracy: 0.9782\n",
            "Epoch 300/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0158 - accuracy: 0.9958 - val_loss: 0.1819 - val_accuracy: 0.9430\n",
            "Epoch 301/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0154 - accuracy: 0.9957 - val_loss: 0.3080 - val_accuracy: 0.9323\n",
            "Epoch 302/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0160 - accuracy: 0.9953 - val_loss: 0.0672 - val_accuracy: 0.9765\n",
            "Epoch 303/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0144 - accuracy: 0.9967 - val_loss: 0.0367 - val_accuracy: 0.9875\n",
            "Epoch 304/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0135 - accuracy: 0.9965 - val_loss: 0.0428 - val_accuracy: 0.9828\n",
            "Epoch 305/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0163 - accuracy: 0.9962 - val_loss: 0.0747 - val_accuracy: 0.9733\n",
            "Epoch 306/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0148 - accuracy: 0.9968 - val_loss: 0.1537 - val_accuracy: 0.9612\n",
            "Epoch 307/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0164 - accuracy: 0.9957 - val_loss: 0.3810 - val_accuracy: 0.9263\n",
            "Epoch 308/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0168 - accuracy: 0.9962 - val_loss: 0.6795 - val_accuracy: 0.8815\n",
            "Epoch 309/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0162 - accuracy: 0.9953 - val_loss: 0.1479 - val_accuracy: 0.9615\n",
            "Epoch 310/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0152 - accuracy: 0.9958 - val_loss: 1.0669 - val_accuracy: 0.8128\n",
            "Epoch 311/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0161 - accuracy: 0.9970 - val_loss: 1.4988 - val_accuracy: 0.7985\n",
            "Epoch 312/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0169 - accuracy: 0.9952 - val_loss: 0.4492 - val_accuracy: 0.8968\n",
            "Epoch 313/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0156 - accuracy: 0.9960 - val_loss: 0.3415 - val_accuracy: 0.9088\n",
            "Epoch 314/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0159 - accuracy: 0.9958 - val_loss: 0.1076 - val_accuracy: 0.9737\n",
            "Epoch 315/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.3730 - val_accuracy: 0.8950\n",
            "Epoch 316/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0149 - accuracy: 0.9962 - val_loss: 0.4682 - val_accuracy: 0.9015\n",
            "Epoch 317/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0147 - accuracy: 0.9968 - val_loss: 0.1377 - val_accuracy: 0.9645\n",
            "Epoch 318/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0158 - accuracy: 0.9958 - val_loss: 1.6066 - val_accuracy: 0.7948\n",
            "Epoch 319/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0147 - accuracy: 0.9967 - val_loss: 0.4499 - val_accuracy: 0.9003\n",
            "Epoch 320/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0140 - accuracy: 0.9963 - val_loss: 2.3850 - val_accuracy: 0.6885\n",
            "Epoch 321/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0158 - accuracy: 0.9953 - val_loss: 0.5503 - val_accuracy: 0.9140\n",
            "Epoch 322/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0167 - accuracy: 0.9960 - val_loss: 3.1180 - val_accuracy: 0.6540\n",
            "Epoch 323/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0143 - accuracy: 0.9963 - val_loss: 1.1085 - val_accuracy: 0.8362\n",
            "Epoch 324/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0217 - accuracy: 0.9930 - val_loss: 1.0357 - val_accuracy: 0.8295\n",
            "Epoch 325/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0161 - accuracy: 0.9958 - val_loss: 0.1317 - val_accuracy: 0.9638\n",
            "Epoch 326/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0160 - accuracy: 0.9972 - val_loss: 0.2696 - val_accuracy: 0.9040\n",
            "Epoch 327/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0172 - accuracy: 0.9953 - val_loss: 0.0252 - val_accuracy: 0.9912\n",
            "Epoch 328/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0168 - accuracy: 0.9960 - val_loss: 0.1115 - val_accuracy: 0.9685\n",
            "Epoch 329/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0151 - accuracy: 0.9968 - val_loss: 0.0931 - val_accuracy: 0.9708\n",
            "Epoch 330/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0150 - accuracy: 0.9965 - val_loss: 0.0525 - val_accuracy: 0.9797\n",
            "Epoch 331/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0138 - accuracy: 0.9968 - val_loss: 0.3968 - val_accuracy: 0.9005\n",
            "Epoch 332/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0181 - accuracy: 0.9962 - val_loss: 0.1669 - val_accuracy: 0.9533\n",
            "Epoch 333/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0187 - accuracy: 0.9957 - val_loss: 5.3249 - val_accuracy: 0.5512\n",
            "Epoch 334/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0142 - accuracy: 0.9963 - val_loss: 0.0240 - val_accuracy: 0.9915\n",
            "Epoch 335/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0135 - accuracy: 0.9972 - val_loss: 0.0732 - val_accuracy: 0.9718\n",
            "Epoch 336/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0152 - accuracy: 0.9967 - val_loss: 0.8057 - val_accuracy: 0.8358\n",
            "Epoch 337/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0128 - accuracy: 0.9973 - val_loss: 0.4544 - val_accuracy: 0.9102\n",
            "Epoch 338/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0156 - accuracy: 0.9960 - val_loss: 0.3923 - val_accuracy: 0.9163\n",
            "Epoch 339/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.3278 - val_accuracy: 0.9243\n",
            "Epoch 340/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0123 - accuracy: 0.9977 - val_loss: 0.0487 - val_accuracy: 0.9835\n",
            "Epoch 341/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.0611 - val_accuracy: 0.9827\n",
            "Epoch 342/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0123 - accuracy: 0.9973 - val_loss: 0.0292 - val_accuracy: 0.9883\n",
            "Epoch 343/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0152 - accuracy: 0.9970 - val_loss: 0.1225 - val_accuracy: 0.9643\n",
            "Epoch 344/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0126 - accuracy: 0.9968 - val_loss: 0.3769 - val_accuracy: 0.9055\n",
            "Epoch 345/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0147 - accuracy: 0.9970 - val_loss: 0.0652 - val_accuracy: 0.9748\n",
            "Epoch 346/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0130 - accuracy: 0.9972 - val_loss: 1.3498 - val_accuracy: 0.8118\n",
            "Epoch 347/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0152 - accuracy: 0.9960 - val_loss: 0.0604 - val_accuracy: 0.9768\n",
            "Epoch 348/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0139 - accuracy: 0.9967 - val_loss: 0.0896 - val_accuracy: 0.9738\n",
            "Epoch 349/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0172 - accuracy: 0.9968 - val_loss: 1.4365 - val_accuracy: 0.8428\n",
            "Epoch 350/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0130 - accuracy: 0.9973 - val_loss: 0.1417 - val_accuracy: 0.9615\n",
            "Epoch 351/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0132 - accuracy: 0.9970 - val_loss: 0.0959 - val_accuracy: 0.9687\n",
            "Epoch 352/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0133 - accuracy: 0.9973 - val_loss: 0.3935 - val_accuracy: 0.9080\n",
            "Epoch 353/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0135 - accuracy: 0.9967 - val_loss: 0.4026 - val_accuracy: 0.9090\n",
            "Epoch 354/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0137 - accuracy: 0.9968 - val_loss: 0.2928 - val_accuracy: 0.9300\n",
            "Epoch 355/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0164 - accuracy: 0.9958 - val_loss: 0.1799 - val_accuracy: 0.9372\n",
            "Epoch 356/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0169 - accuracy: 0.9965 - val_loss: 0.9949 - val_accuracy: 0.8135\n",
            "Epoch 357/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0120 - accuracy: 0.9975 - val_loss: 0.5154 - val_accuracy: 0.9235\n",
            "Epoch 358/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0133 - accuracy: 0.9968 - val_loss: 0.0703 - val_accuracy: 0.9763\n",
            "Epoch 359/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.1175 - val_accuracy: 0.9698\n",
            "Epoch 360/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0134 - accuracy: 0.9960 - val_loss: 0.1647 - val_accuracy: 0.9545\n",
            "Epoch 361/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0134 - accuracy: 0.9970 - val_loss: 0.0418 - val_accuracy: 0.9852\n",
            "Epoch 362/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0130 - accuracy: 0.9965 - val_loss: 0.5922 - val_accuracy: 0.8910\n",
            "Epoch 363/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0120 - accuracy: 0.9975 - val_loss: 0.1073 - val_accuracy: 0.9702\n",
            "Epoch 364/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0130 - accuracy: 0.9967 - val_loss: 0.1973 - val_accuracy: 0.9523\n",
            "Epoch 365/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0115 - accuracy: 0.9978 - val_loss: 0.0224 - val_accuracy: 0.9912\n",
            "Epoch 366/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0174 - accuracy: 0.9958 - val_loss: 0.2920 - val_accuracy: 0.9285\n",
            "Epoch 367/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0173 - accuracy: 0.9953 - val_loss: 0.1411 - val_accuracy: 0.9632\n",
            "Epoch 368/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0175 - accuracy: 0.9953 - val_loss: 4.3914 - val_accuracy: 0.6680\n",
            "Epoch 369/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0122 - accuracy: 0.9967 - val_loss: 1.5889 - val_accuracy: 0.7812\n",
            "Epoch 370/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.0438 - val_accuracy: 0.9867\n",
            "Epoch 371/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0131 - accuracy: 0.9962 - val_loss: 0.3370 - val_accuracy: 0.9187\n",
            "Epoch 372/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0146 - accuracy: 0.9972 - val_loss: 0.0560 - val_accuracy: 0.9827\n",
            "Epoch 373/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0120 - accuracy: 0.9972 - val_loss: 0.0193 - val_accuracy: 0.9922\n",
            "Epoch 374/400\n",
            "188/188 [==============================] - 16s 84ms/step - loss: 0.0131 - accuracy: 0.9960 - val_loss: 0.1653 - val_accuracy: 0.9560\n",
            "Epoch 375/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0113 - accuracy: 0.9965 - val_loss: 0.0199 - val_accuracy: 0.9928\n",
            "Epoch 376/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0128 - accuracy: 0.9968 - val_loss: 0.0436 - val_accuracy: 0.9852\n",
            "Epoch 377/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 0.4580 - val_accuracy: 0.9025\n",
            "Epoch 378/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0120 - accuracy: 0.9970 - val_loss: 0.2141 - val_accuracy: 0.9592\n",
            "Epoch 379/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0138 - accuracy: 0.9963 - val_loss: 0.2773 - val_accuracy: 0.9357\n",
            "Epoch 380/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0128 - accuracy: 0.9970 - val_loss: 0.6330 - val_accuracy: 0.8767\n",
            "Epoch 381/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0118 - accuracy: 0.9972 - val_loss: 0.1075 - val_accuracy: 0.9698\n",
            "Epoch 382/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0113 - accuracy: 0.9968 - val_loss: 0.2586 - val_accuracy: 0.9387\n",
            "Epoch 383/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0135 - accuracy: 0.9967 - val_loss: 0.1951 - val_accuracy: 0.9442\n",
            "Epoch 384/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0100 - accuracy: 0.9975 - val_loss: 0.3153 - val_accuracy: 0.9212\n",
            "Epoch 385/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0108 - accuracy: 0.9973 - val_loss: 0.2989 - val_accuracy: 0.9195\n",
            "Epoch 386/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0111 - accuracy: 0.9973 - val_loss: 0.7294 - val_accuracy: 0.8472\n",
            "Epoch 387/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0099 - accuracy: 0.9978 - val_loss: 0.1702 - val_accuracy: 0.9605\n",
            "Epoch 388/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0117 - accuracy: 0.9967 - val_loss: 0.0349 - val_accuracy: 0.9895\n",
            "Epoch 389/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 1.0379 - val_accuracy: 0.8282\n",
            "Epoch 390/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0103 - accuracy: 0.9973 - val_loss: 0.0779 - val_accuracy: 0.9725\n",
            "Epoch 391/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0131 - accuracy: 0.9972 - val_loss: 1.7850 - val_accuracy: 0.7967\n",
            "Epoch 392/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0098 - accuracy: 0.9973 - val_loss: 0.1895 - val_accuracy: 0.9603\n",
            "Epoch 393/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0102 - accuracy: 0.9972 - val_loss: 0.0087 - val_accuracy: 0.9970\n",
            "Epoch 394/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0109 - accuracy: 0.9972 - val_loss: 0.0771 - val_accuracy: 0.9753\n",
            "Epoch 395/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0138 - accuracy: 0.9960 - val_loss: 0.3019 - val_accuracy: 0.9220\n",
            "Epoch 396/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0104 - accuracy: 0.9975 - val_loss: 0.0514 - val_accuracy: 0.9827\n",
            "Epoch 397/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0100 - accuracy: 0.9977 - val_loss: 0.1879 - val_accuracy: 0.9487\n",
            "Epoch 398/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0093 - accuracy: 0.9982 - val_loss: 0.0480 - val_accuracy: 0.9850\n",
            "Epoch 399/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0104 - accuracy: 0.9970 - val_loss: 0.0453 - val_accuracy: 0.9858\n",
            "Epoch 400/400\n",
            "188/188 [==============================] - 16s 83ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.2039 - val_accuracy: 0.9452\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(Gamma_container, intLabels30, validation_data=(Gamma_container_val, intValLabels30),  epochs=400, callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kKmeTmL8u6VJ",
        "outputId": "cde7e9f2-5bd9-4b34-ea25-7762e9498dd1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1d996386970>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "plt.subplot(1,2,1)\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Testing"
      ],
      "metadata": {
        "id": "sPj9kIpEVZJ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, we test the models with two types of accuracy:\n",
        "1.   The accuracy of the method *evaluate()* for which a prediction correct if and only if it is equal to the label\n",
        "2.   The custom accuracy defined in the aforementioned paper where a prediction is considered correct also with an error of $\\pm$ 1 class. E.g.: if the label indicates 10, I will also consider class 9 and 11 as a correct prediction.\n",
        "\n"
      ],
      "metadata": {
        "id": "GrQhc27UBmG1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Model for resolution 10°"
      ],
      "metadata": {
        "id": "_76COAl4VGFa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Move into folder containing the test set for res 10°\n",
        "%cd /content/drive/MyDrive/Github/CNN_DOA/Dataset/MATLAB_TEST10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ij4KGdBkVRm7",
        "outputId": "bc255f7f-6de8-4b5e-d3da-cac1f6070fc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1-B6wbQJimyIAQNh3Ccp578yl6uudDF9j/CNN_DOA/Dataset/MATLAB_TEST10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jEF_crimu6VK",
        "outputId": "1a1a1fbc-e4ac-445b-9773-c32acaea9155",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 360/360 [00:14<00:00, 24.84it/s] \n",
            "  0%|          | 0/360 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/librosa/util/utils.py:200: UserWarning: librosa.util.frame called with axis=-1 on a non-contiguous input. This will result in a copy.\n",
            "  \"on a non-contiguous input. This will result in a copy.\".format(axis)\n",
            "100%|██████████| 360/360 [00:03<00:00, 109.50it/s]\n"
          ]
        }
      ],
      "source": [
        "# Extract features\n",
        "audiofiles_test = [str(file) for file in Path().glob('SA*.wav')]\n",
        "audiofiles_test = sorted_nicely(audiofiles_test)\n",
        "\n",
        "sr = 16000\n",
        "duration = 0.5\n",
        "\n",
        "testAudioMatrix, sourceIDs, testlabels30, testlabels10 = extractAudiodata(audiofiles_test, sr*duration)\n",
        "\n",
        "datapointsNum = testAudioMatrix.shape[-1]\n",
        "Gamma_container_test = np.zeros([datapointsNum, 14, 511, 10])\n",
        "\n",
        "\n",
        "for i in tqdm(range(0, datapointsNum)):\n",
        "  Gamma_container_test[i,:,:,:] = featureExtractor2(testAudioMatrix[:,:,i])\n",
        "\n",
        "intTestLabels10 = np.array(testlabels10, dtype='uint8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MBd7W1bhu6VK",
        "outputId": "7a8a258a-9ca9-4fce-d9eb-d2c26e957d2e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1-B6wbQJimyIAQNh3Ccp578yl6uudDF9j/CNN_DOA/models\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Github/CNN_DOA/models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gQqdTOMsu6VK"
      },
      "outputs": [],
      "source": [
        "# Load the best model\n",
        "model = keras.models.load_model('modelRes10')\n",
        "\n",
        "# Compile\n",
        "learning_rate = 1e-5 \n",
        "optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r903p43Gu6VL",
        "outputId": "4dd6c123-8094-41fc-cf9c-357b4f1714eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 7s 522ms/step - loss: 0.1941 - accuracy: 0.9500\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.19411765038967133, 0.949999988079071]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Accuracy\n",
        "model.evaluate(Gamma_container_test, intTestLabels10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5mvyBd7ru6VL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dc3cfac-e4dc-4f86-98cc-1fe1214d986c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
            " 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.]\n"
          ]
        }
      ],
      "source": [
        "preds = model.predict(Gamma_container_test)\n",
        "\n",
        "predsCategorical = np.zeros([preds.shape[0]])\n",
        "for i in range(0, preds.shape[0]):\n",
        "  predClass = np.argmax(preds[i])\n",
        "  predsCategorical[i] = predClass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0WJ7UTEu6VL",
        "outputId": "10cfe3a9-a12a-43f6-dc54-395af240ae8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "0.0 in range [35  0  1]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "9.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "11.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "12.0 in range [11 12 13]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "15.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "13.0 in range [12 13 14]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "13.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "14.0 in range [13 14 15]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [14 15 16]\n",
            "15.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "15.0 in range [15 16 17]\n",
            "16.0 in range [15 16 17]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "17.0 in range [16 17 18]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "18.0 in range [17 18 19]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "19.0 in range [18 19 20]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "20.0 in range [19 20 21]\n",
            "17.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "22.0 in range [20 21 22]\n",
            "22.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "21.0 in range [20 21 22]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "22.0 in range [21 22 23]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "23.0 in range [22 23 24]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "24.0 in range [23 24 25]\n",
            "26.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "25.0 in range [24 25 26]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "26.0 in range [25 26 27]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "28.0 in range [26 27 28]\n",
            "26.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "27.0 in range [26 27 28]\n",
            "28.0 in range [27 28 29]\n",
            "29.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "27.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "28.0 in range [27 28 29]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "29.0 in range [28 29 30]\n",
            "30.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "32.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "33.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "30.0 in range [29 30 31]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "31.0 in range [30 31 32]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "32.0 in range [31 32 33]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "32.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "33.0 in range [32 33 34]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "34.0 in range [33 34 35]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n",
            "35.0 in range [34 35  0]\n"
          ]
        }
      ],
      "source": [
        "# Evaluating with custom accuracity (adjacent classes are considered true positives)\n",
        "customAcc = 0\n",
        "\n",
        "for i in range(0, predsCategorical.shape[0]):\n",
        "    validRange = np.array([intTestLabels10[i,0]-1, intTestLabels10[i,0], intTestLabels10[i,0]+1])\n",
        "    for j in range(validRange.shape[0]):\n",
        "        if validRange[j] == -1:\n",
        "            validRange[j] = 35\n",
        "        if validRange[j] == 36:\n",
        "            validRange[j] = 0\n",
        "    print('{} in range {}'.format(predsCategorical[i], validRange))\n",
        "    if predsCategorical[i] in validRange:\n",
        "        #print('{} in range {}'.format(intTestLabels10[i], validRange))\n",
        "        customAcc += 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customAcc = customAcc / predsCategorical.shape[0] * 100\n",
        "print('Custom accuracy of {}%'.format(customAcc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OR5kbhSBXdm1",
        "outputId": "9818cb9b-9620-40ca-fb29-8d90f48b0946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Custom accuracy of 98.88888888888889%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Model for resolution 30°"
      ],
      "metadata": {
        "id": "MwMok3q7XP_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Move into folder containing the test set for res 10°\n",
        "%cd /content/drive/MyDrive/Github/CNN_DOA/Dataset/MATLAB_TEST30"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCfB9S_iXjVt",
        "outputId": "63dfab55-5e5b-4c36-a1c9-15913d427da1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1-B6wbQJimyIAQNh3Ccp578yl6uudDF9j/CNN_DOA/Dataset/MATLAB_TEST30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sk26aJSIu6VP",
        "outputId": "e6a7e8c0-6cf4-43a4-cdec-1410a6572c11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 120/120 [00:05<00:00, 22.30it/s]\n",
            "  0%|          | 0/120 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/librosa/util/utils.py:200: UserWarning: librosa.util.frame called with axis=-1 on a non-contiguous input. This will result in a copy.\n",
            "  \"on a non-contiguous input. This will result in a copy.\".format(axis)\n",
            "100%|██████████| 120/120 [00:01<00:00, 102.18it/s]\n"
          ]
        }
      ],
      "source": [
        "# Get list of audio files in the current folder\n",
        "audiofiles_test = [str(file) for file in Path().glob('SA*.wav')]\n",
        "audiofiles_test = sorted_nicely(audiofiles_test)\n",
        "\n",
        "sr = 16000\n",
        "duration = 0.5\n",
        "\n",
        "testAudioMatrix, sourceIDs, testlabels30, testlabels10 = extractAudiodata(audiofiles_test, sr*duration)\n",
        "\n",
        "datapointsNum = testAudioMatrix.shape[-1]\n",
        "Gamma_container_test = np.zeros([datapointsNum, 14, 511, 10])\n",
        "\n",
        "for i in tqdm(range(0, datapointsNum)):\n",
        "  Gamma_container_test[i,:,:,:] = featureExtractor2(testAudioMatrix[:,:,i])\n",
        "\n",
        "intTestLabels30 = np.array(testlabels30, dtype='uint8')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/Github/CNN_DOA/models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5CZ1PdLXr2V",
        "outputId": "296908ac-15db-4aa0-a516-c62abb91ffb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1-B6wbQJimyIAQNh3Ccp578yl6uudDF9j/CNN_DOA/models\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.models.load_model('modelRes30')\n",
        "\n",
        "# Compile\n",
        "learning_rate = 1e-5 \n",
        "optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "fvZThfZ-X2Ba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ERC3FAXSu6VP",
        "outputId": "7ffd4ffa-fed8-4acd-d5a8-9c4418822d87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 3s 538ms/step - loss: 0.0316 - accuracy: 0.9917\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.031580325216054916, 0.9916666746139526]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model.evaluate(Gamma_container_test, intTestLabels30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mTWFkfGgu6VP",
        "outputId": "1ca79dc1-9019-441c-9249-6c15a8914543",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "0.0 in range [11  0  1]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "1.0 in range [0 1 2]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "2.0 in range [1 2 3]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "3.0 in range [2 3 4]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "4.0 in range [3 4 5]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "5.0 in range [4 5 6]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "6.0 in range [5 6 7]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "6.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "7.0 in range [6 7 8]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "8.0 in range [7 8 9]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "9.0 in range [ 8  9 10]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "10.0 in range [ 9 10 11]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n",
            "11.0 in range [10 11 12]\n"
          ]
        }
      ],
      "source": [
        "preds = model.predict(Gamma_container_test)\n",
        "\n",
        "predsCategorical = np.zeros([preds.shape[0]])\n",
        "for i in range(0, preds.shape[0]):\n",
        "  predClass = np.argmax(preds[i])\n",
        "  predsCategorical[i] = predClass\n",
        "\n",
        "customAcc = 0\n",
        "\n",
        "for i in range(0, predsCategorical.shape[0]):\n",
        "    validRange = np.array([intTestLabels30[i,0]-1, intTestLabels30[i,0], intTestLabels30[i,0]+1])\n",
        "    for j in range(validRange.shape[0]):\n",
        "        if validRange[j] == -1:\n",
        "            validRange[j] = 11\n",
        "        if validRange[j] == 13:\n",
        "            validRange[j] = 0\n",
        "    print('{} in range {}'.format(predsCategorical[i], validRange))\n",
        "    if predsCategorical[i] in validRange:\n",
        "        #print('{} in range {}'.format(intTestLabels10[i], validRange))\n",
        "        customAcc += 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customAcc = customAcc / predsCategorical.shape[0] * 100\n",
        "print('Custom accuracy of {}%'.format(customAcc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFMbsc5gYFif",
        "outputId": "90c94c0c-6234-4c85-dea8-7d5a2f401532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Custom accuracy of 100.0%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "f567ae853ad6f7787493655373850248a71705b332143b104b76d40991e3d1ce"
    },
    "kernelspec": {
      "display_name": "Python 3.9.12 ('PC')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "orig_nbformat": 4,
    "colab": {
      "name": "Neural Net Training.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}