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CatDog_Classifier_92TestAccuracy.py
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CatDog_Classifier_92TestAccuracy.py
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#!/usr/bin/env python
# coding: utf-8
# In[2]:
# Import libraries
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import classification_report
from tensorflow.keras.preprocessing import image
import numpy as np
# In[3]:
# Specify path to training, validation, and testing data
train_path = 'Train_80_10_10/train'
val_path = 'Train_80_10_10/val'
test_path = 'Train_80_10_10/test'
# In[4]:
# Normalize images (you can also augment training dataset if needed here)
train_datagen = ImageDataGenerator(rescale=1/255, shear_range=0.1, zoom_range=0.1, horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1/255)
test_datagen = ImageDataGenerator(rescale=1/255)
# In[5]:
Image_width = 180
Image_height = 180
Image_channels = 3
target_size = (Image_width,Image_height)
# Prepare batches of data for training
train_data_gen = train_datagen.flow_from_directory(train_path,
target_size=target_size,
batch_size=64,
class_mode='categorical',
shuffle=True)
# Prepare batches of data for validation
val_data_gen = val_datagen.flow_from_directory(val_path,
target_size=target_size,
batch_size=16,
class_mode='categorical',
shuffle=True)
# Prepare batches of data for testing on unknown samples (not used for training/validation)
test_data_gen = test_datagen.flow_from_directory(test_path,
target_size=target_size,
batch_size=16,
class_mode='categorical',
shuffle=True)
# In[6]:
# The next function returns a batch from the training dataset. We use image data and discard the labels.
sample_training_images, _ = next(train_data_gen)
# This function will plot images in the form of a grid with 4 rows and 4 columns
def plotImages(images_arr):
fig, axes = plt.subplots(4, 4, figsize=(10,10))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
ax.axis('off')
plt.tight_layout()
plt.show()
# Plot 16 random images from training data
plotImages(sample_training_images[:16])
# In[7]:
model = Sequential()
model.add(Conv2D(input_shape=(Image_width, Image_height, Image_channels),filters=32,kernel_size=(3,3),padding="same", activation="relu"))
model.add(Conv2D(filters=32,kernel_size=(3,3),padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(units=512,activation="relu"))
model.add(Dense(units=512,activation="relu"))
model.add(Dense(units=2, activation="softmax"))
model.summary()
# # Build your CNN model
#
# model = Sequential()
#
#
# model.add(Conv2D(32, (3, 3), input_shape=(Image_width, Image_height, Image_channels), activation='relu'))
# model.add(Conv2D(32, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Conv2D(64, (3, 3), activation='relu'))
# model.add(Conv2D(64, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Conv2D(128, (3, 3), activation='relu'))
# model.add(Conv2D(128, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Conv2D(256, (3, 3), activation='relu'))
# model.add(Conv2D(256, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Flatten())
# model.add(Dense(256, activation='relu'))
# model.add(Dropout(0.5))
#
# model.add(Dense(256, activation='relu'))
# model.add(Dropout(0.5))
#
# model.add(Dense(2))
# model.add(Activation('softmax'))
#
# model.summary()
# In[7]:
model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.0001), metrics=['accuracy'])
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("CatDogClassifier.h5", monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=20, verbose=1, mode='auto')
history = model.fit_generator(steps_per_epoch=128,generator=train_data_gen, validation_data= val_data_gen, validation_steps=50,epochs=50,callbacks=[checkpoint,early])
# In[9]:
# Plot model accuracy and loss
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(50)
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
# In[17]:
from tensorflow.keras.models import load_model
saved_model = load_model("CatDogClassifier.h5")
test_acc = saved_model.evaluate(test_data_gen, steps=50)
# In[38]:
target_names = []
for key in train_data_gen.class_indices:
target_names.append(key)
print(target_names)
# In[51]:
img = image.load_img("cat_1.jpg",target_size=(180,180))
img = np.asarray(img)
plt.imshow(img)
img = np.expand_dims(img, axis=0)
output = saved_model.predict(img)
if output[0][0] >= output[0][1]:
print("Cat")
else:
print('Dog')
# In[ ]: