From ef29b73ab5a5bb91a0d786f0962600fb049a11e7 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Fri, 20 Sep 2024 01:30:03 -0500 Subject: [PATCH 01/31] hard work --- model/intent/autoencoder_reconstruct.png | Bin 0 -> 315110 bytes model/intent/edf_parser.py | 18 +++++- model/intent/edf_train.py | 46 +++++++------- model/intent/model.py | 76 ++++++++++++++++------- model/intent/physionet_decoder.keras | Bin 126449 -> 54765 bytes model/intent/physionet_encoder.keras | Bin 114807 -> 58044 bytes model/intent/pipeline.py | 3 +- model/intent/train.py | 7 ++- 8 files changed, 100 insertions(+), 50 deletions(-) create mode 100644 model/intent/autoencoder_reconstruct.png diff --git a/model/intent/autoencoder_reconstruct.png b/model/intent/autoencoder_reconstruct.png new file mode 100644 index 0000000000000000000000000000000000000000..af192c15c907ab642740d435a94d256d5b3bb995 GIT binary patch literal 315110 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raw.info['sfreq'] # Identify T0, T1, T2 selected_events = events[(events[:, 2] == event_id['T1']) | (events[:, 2] == event_id['T2']) | (events[:, 2] == event_id['T0'])] + # Create Synthetic Events to get the whole minute + start_event_sample = selected_events[0, 0] + synthetic_events = np.array([ + [int(start_event_sample + i * sfreq), 0, 1] # Each event 1 second apart + for i in range(60) + ]) + # Create epochs around these events - epochs = mne.Epochs(raw, selected_events, tmin=0, tmax=1.0, preload=True, baseline=None) + epochs = mne.Epochs(raw, synthetic_events, tmin=0, tmax=1.0, preload=True, baseline=None) # Convert epochs to NumPy arrays return epochs.get_data() diff --git a/model/intent/edf_train.py b/model/intent/edf_train.py index ffa6466..2e12b94 100644 --- a/model/intent/edf_train.py +++ b/model/intent/edf_train.py @@ -1,13 +1,10 @@ import numpy as np from keras.optimizers import Adam -from keras.models import Sequential from keras.callbacks import EarlyStopping from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler as Scaler -from model import encoder, decoder - -import pickle +from model import auto_encoder # Load the data data = np.load('dataset.pkl') @@ -27,23 +24,21 @@ X_train, X_val = train_test_split(data, test_size=0.2) # Build the autoencoder -autoencoder = Sequential([ - encoder, - decoder -]) +autoencoder = auto_encoder autoencoder.compile(optimizer=Adam(learning_rate=0.001), loss='huber') # Define the EarlyStopping callback -early_stopping = EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True, verbose=0) +early_stopping = EarlyStopping(monitor='val_loss', patience=2*3, restore_best_weights=True, verbose=0) # Train the autoencoder with early stopping -batch_size = 512 +batch_size = 256 epochs = 128 fit_history = autoencoder.fit( X_train, X_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, X_val), - callbacks=[early_stopping], verbose=1 + callbacks=[early_stopping], + verbose=1 ) #Save the model @@ -67,18 +62,25 @@ reconstructed = autoencoder.predict(X_val) +X_val = X_val.transpose(0, 2, 1) +reconstructed = reconstructed.transpose(0, 2, 1) + i = random.randint(0, len(X_val) - 1) js = list(range(0, 64)) random.shuffle(js) -js = js[:4] -original = X_val[i][js].flatten() -reconstructed_sample = reconstructed[i][js].flatten() - -plt.figure(figsize=(12, 4)) -plt.subplot(1, 2, 1) -plt.plot(original) -plt.title('Original Data') -plt.subplot(1, 2, 2) -plt.plot(reconstructed_sample) -plt.title('Reconstructed Data') +js = js[:4] # Select 4 random channels +original = X_val[i][js] +reconstructed_sample = reconstructed[i][js] + +# Create subplots for each selected channel +fig, axs = plt.subplots(len(js), 1, figsize=(9, 16)) + +# Plot the original and reconstructed signals for each channel +for idx, j in enumerate(js): + axs[idx].plot(original[idx], label='original') + axs[idx].plot(reconstructed_sample[idx], label='reconstructed') + axs[idx].set_title(f'Channel {j} Reconstruction Comparison') + axs[idx].legend(loc='upper left') + +plt.tight_layout() plt.savefig('autoencoder_reconstruct.png') \ No newline at end of file diff --git a/model/intent/model.py b/model/intent/model.py index 62abaff..aaa9cc7 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -2,8 +2,8 @@ import keras from keras.models import Sequential -from keras.layers import Dense, Activation, Flatten, Multiply, BatchNormalization, Dropout, Layer -from keras.layers import SeparableConv1D, Conv1D, UpSampling1D, MaxPooling1D +from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D +from keras.layers import Activation, Flatten, Multiply, BatchNormalization, Dropout ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -29,42 +29,72 @@ def call(self, inputs): ## Encoder and Decoder Trained on the physionet motor imagery dataset ## https://www.physionet.org/content/eegmmidb/1.0.0/ ## Thanks again to Summer, Programmerboi, Hosomi +## Modification to follow along this paper +## https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522466/ -act = 'silu' +@keras.saving.register_keras_serializable() +class ExpandDimsLayer(Layer): + def call(self, inputs): + return tf.expand_dims(inputs, axis=-1) + +@keras.saving.register_keras_serializable() +class SqueezeDimsLayer(Layer): + def call(self, inputs): + return tf.squeeze(inputs, axis=-1) + +kernel = (3, 3) +act = 'leaky_relu' + +encoder = Sequential([ + ExpandDimsLayer(), + SeparableConv2D(16, kernel, padding='same'), + DepthwiseConv2D(kernel, padding='same', strides=2), + BatchNormalization(), Activation(act), + + SeparableConv2D(16, kernel, padding='same'), + DepthwiseConv2D(kernel, padding='same', strides=2), + BatchNormalization(), Activation(act), + + SeparableConv2D(16, kernel, padding='same'), + DepthwiseConv2D(kernel, padding='same', strides=2), + BatchNormalization(), Activation(act), -encoder = Sequential([ - SeparableConv1D(128, 3, padding='same'), - BatchNormalization(), Activation(act), MaxPooling1D(2), - SeparableConv1D(64, 3, padding='same'), - BatchNormalization(), Activation(act), MaxPooling1D(2), - SeparableConv1D(32, 3, padding='same'), - Activation(act) + SeparableConv2D(16, kernel, padding='same'), ]) decoder = Sequential([ - SeparableConv1D(32, 3, padding='same'), - BatchNormalization(), Activation(act), UpSampling1D(2), - SeparableConv1D(64, 3, padding='same'), - BatchNormalization(), Activation(act), UpSampling1D(2), - SeparableConv1D(128, 3, padding='same'), - BatchNormalization(), Activation(act), + SeparableConv2D(16, kernel, padding='same'), + BatchNormalization(), Activation(act), UpSampling2D(2), + + SeparableConv2D(16, kernel, padding='same'), + BatchNormalization(), Activation(act), UpSampling2D(2), - SeparableConv1D(64, 1, padding='same', activation='sigmoid'), + SeparableConv2D(16, kernel, padding='same'), + BatchNormalization(), Activation(act), UpSampling2D(2), + + SeparableConv2D(1, kernel, padding='same'), Activation('relu'), + SqueezeDimsLayer() +]) + +auto_encoder = Sequential([ + encoder, + decoder ]) ## First Layer to convert any channels to 64 ranged [0, 1] -def create_first_layer(channels, expanded_channels=64): +def create_first_layer(chs=64): return Sequential([ - SeparableConv1D(expanded_channels, channels, padding='same', use_bias=False), - BatchNormalization(), - Activation('sigmoid'), - Dropout(0.1), + Conv1D(chs//4, 3, padding='same'), + BatchNormalization(), Activation('silu'), + Conv1D(chs//2, 3, padding='same'), + BatchNormalization(), Activation('silu'), + Conv1D(chs//1, 3, padding='same'), + BatchNormalization(), Activation('sigmoid'), ]) ## Last Layer to map latent space to custom classes def create_last_layer(classes): return Sequential([ - SpatialAttention(classes, 5), Flatten(), Dropout(0.1), Dense(classes, activation='softmax', kernel_regularizer='l2') diff --git 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z-N*jqxa@a?YT(V=XN11h+ECw|$p%NAXU(d<@%4AgBYWkP=!nNX{-S%HsHA0$^S6lD z&li*Jq;jt?EuK$=_v1~_-ESCk(AP_J@_9IiNBGC@zs32g>aM+qyMNW%?eI_e=QZ%V z{`QPxRo(j2le$NnU3wz^i}H8-bpxMR3%lrpHE*5I=co5 zxX>Sez5dlx$NZ^c7Y9W6&GU=!GhY}U8uI(4TSjR=N*^F5_Q#*%pIy3;3vuqgfeV8E z&4r3V)YI|#oMChJi;Mm7_b=lgcS(%6zNVPi;?Q{k!RE^Y76i?Y2sfW^b&wXJ4 diff --git a/model/intent/pipeline.py b/model/intent/pipeline.py index 0ed2bc8..f7162d4 100644 --- a/model/intent/pipeline.py +++ b/model/intent/pipeline.py @@ -13,7 +13,8 @@ def preprocess_data(session_data, sampling_rate): for eeg_chan in range(len(session_data)): DataFilter.detrend(session_data[eeg_chan], DetrendOperations.LINEAR) DataFilter.remove_environmental_noise(session_data[eeg_chan], sampling_rate, NoiseTypes.FIFTY_AND_SIXTY.value) - DataFilter.perform_lowpass(session_data[eeg_chan], sampling_rate, 80, 4, FilterTypes.BUTTERWORTH.value, 0) # resample effect mitigation + # bandpass to alpha, beta, gamma, 80 for resample effect mitigation + DataFilter.perform_bandpass(session_data[eeg_chan], sampling_rate, 8, 80, 4, FilterTypes.BUTTERWORTH.value, 0) return session_data def extract_features(preprocessed_data): diff --git a/model/intent/train.py b/model/intent/train.py index 1483330..4bf70d7 100644 --- a/model/intent/train.py +++ b/model/intent/train.py @@ -142,10 +142,9 @@ def process_windows(windows): ## create channel expander/normalizer and classification layer classes = len(processed_windows) - user_channels = len(eeg_channels) encoder_channels = pretrained_encoder.input_shape[-1] - expandalizer = create_first_layer(user_channels, encoder_channels) + expandalizer = create_first_layer(encoder_channels) classifier = create_last_layer(classes) ## Create Model @@ -159,7 +158,7 @@ def process_windows(windows): model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy') ## Set up EarlyStopping - early_stopping = EarlyStopping(monitor='val_loss', patience=2*3, restore_best_weights=True, verbose=0) + early_stopping = EarlyStopping(monitor='val_loss', patience=2*4, restore_best_weights=True, verbose=0) ## Train the model batch_size = 128 @@ -182,6 +181,8 @@ def process_windows(windows): predictions_prob = model.predict(X_test) predictions = np.argmax(predictions_prob, axis=1) y_test_idxs = np.argmax(y_test, axis=1) + print("Model evaluation:") + model.evaluate(X_test, y_test) print(classification_report(y_test_idxs, predictions)) ## Plot history accuracy from model From b8fde605584f858b4f77f61def8721b7906936c0 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Fri, 20 Sep 2024 13:40:20 -0500 Subject: [PATCH 02/31] dilation from paper --- model/intent/autoencoder_reconstruct.png | Bin 315110 -> 324453 bytes model/intent/edf_train.py | 4 +- model/intent/model.py | 46 ++++++++++++----------- model/intent/physionet_decoder.keras | Bin 54765 -> 98360 bytes model/intent/physionet_encoder.keras | Bin 58044 -> 90728 bytes 5 files changed, 26 insertions(+), 24 deletions(-) diff --git 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z{#Bela~h1P`he{g#VE9~!0s{`M*2j(gJH*Uet9{c+gFu^$t#>eNi>$;V#`tLSfdaGXMRKChdLz&s6V!zXIWjDE& Date: Wed, 25 Sep 2024 00:42:54 -0500 Subject: [PATCH 08/31] expand classify tuning --- model/intent/model.py | 13 ++++++++----- model/intent/train.py | 43 ++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 6 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index df501d0..0d555e6 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -3,7 +3,7 @@ from keras.models import Sequential from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D, GlobalAveragePooling2D -from keras.layers import Activation, Flatten, Multiply, BatchNormalization, Dropout +from keras.layers import Activation, Multiply, BatchNormalization ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -38,6 +38,7 @@ class SqueezeDimsLayer(Layer): def call(self, inputs): return tf.squeeze(inputs, axis=-1) +# Noise Layer @keras.utils.register_keras_serializable() class AddNoiseLayer(Layer): def __init__(self, noise_factor=0.1, **kwargs): @@ -100,9 +101,9 @@ def create_block(filters, kernel, dilation_rates, end_stride=1): def create_first_layer(chs=64): return Sequential([ Conv1D(chs//4, 3, padding='same'), - BatchNormalization(), Activation('silu'), + BatchNormalization(), Activation(act), Conv1D(chs//2, 3, padding='same'), - BatchNormalization(), Activation('silu'), + BatchNormalization(), Activation(act), Conv1D(chs//1, 3, padding='same'), BatchNormalization(), Activation('sigmoid'), ]) @@ -110,7 +111,9 @@ def create_first_layer(chs=64): ## Last Layer to map latent space to custom classes def create_last_layer(classes): return Sequential([ - GlobalAveragePooling2D(), - Dropout(0.2), + Sequential([ + GlobalAveragePooling2D(), Dense(16), + BatchNormalization(), Activation(act) + ]), Dense(classes, activation='softmax', kernel_regularizer='l2') ]) \ No newline at end of file diff --git a/model/intent/train.py b/model/intent/train.py index 4bf70d7..aaca469 100644 --- a/model/intent/train.py +++ b/model/intent/train.py @@ -185,6 +185,9 @@ def process_windows(windows): model.evaluate(X_test, y_test) print(classification_report(y_test_idxs, predictions)) + # Use the dark background style + plt.style.use('dark_background') + ## Plot history accuracy from model plt.plot(fit_history.history['loss']) plt.plot(fit_history.history['val_loss']) @@ -192,7 +195,45 @@ def process_windows(windows): plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'val'], loc='upper left') - plt.show() + plt.savefig('loss.png') + + from sklearn.preprocessing import StandardScaler + from sklearn.manifold import TSNE + import tensorflow as tf + + # Assuming `latent` has shape (samples, timesteps, channels, features) + seq = Sequential([ + expandalizer, + pretrained_encoder, + classifier.layers[0] + ]) + latent = seq(X_test) + + # Step 1: Reshape to 2D by flattening the last three dimensions + samples = latent.shape[0] # Number of samples + # Flatten the timesteps, channels, and features dimensions into a single dimension + latent_flat = tf.reshape(latent, (samples, -1)).numpy() + + # Step 2: Standardize the flattened data + scaler = StandardScaler() + X_scaled = scaler.fit_transform(latent_flat) + + # Step 3: Apply t-SNE + tsne = TSNE(n_components=2, perplexity=30, learning_rate=200, n_iter=1000, random_state=42) + X_tsne = tsne.fit_transform(X_scaled) + + # Step 5: Plot the t-SNE result + plt.figure(figsize=(10, 10)) + scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=i_test, cmap='viridis', alpha=0.7) + plt.colorbar(scatter, label='Labels') + plt.title('t-SNE Visualization of Labeled Data') + plt.xlabel('t-SNE Component 1') + plt.ylabel('t-SNE Component 2') + plt.grid(True) + + # Set the scatter plot aspect to be square + plt.axis('square') + plt.savefig('tsne.png') if __name__ == "__main__": From e68735b8316e3ab1c2c8522d6e1f8069f80849eb Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Wed, 25 Sep 2024 00:42:54 -0500 Subject: [PATCH 09/31] expand classify tuning --- model/intent/model.py | 13 ++++++++----- model/intent/train.py | 43 ++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 6 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index df501d0..0d555e6 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -3,7 +3,7 @@ from keras.models import Sequential from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D, GlobalAveragePooling2D -from keras.layers import Activation, Flatten, Multiply, BatchNormalization, Dropout +from keras.layers import Activation, Multiply, BatchNormalization ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -38,6 +38,7 @@ class SqueezeDimsLayer(Layer): def call(self, inputs): return tf.squeeze(inputs, axis=-1) +# Noise Layer @keras.utils.register_keras_serializable() class AddNoiseLayer(Layer): def __init__(self, noise_factor=0.1, **kwargs): @@ -100,9 +101,9 @@ def create_block(filters, kernel, dilation_rates, end_stride=1): def create_first_layer(chs=64): return Sequential([ Conv1D(chs//4, 3, padding='same'), - BatchNormalization(), Activation('silu'), + BatchNormalization(), Activation(act), Conv1D(chs//2, 3, padding='same'), - BatchNormalization(), Activation('silu'), + BatchNormalization(), Activation(act), Conv1D(chs//1, 3, padding='same'), BatchNormalization(), Activation('sigmoid'), ]) @@ -110,7 +111,9 @@ def create_first_layer(chs=64): ## Last Layer to map latent space to custom classes def create_last_layer(classes): return Sequential([ - GlobalAveragePooling2D(), - Dropout(0.2), + Sequential([ + GlobalAveragePooling2D(), Dense(16), + BatchNormalization(), Activation(act) + ]), Dense(classes, activation='softmax', kernel_regularizer='l2') ]) \ No newline at end of file diff --git a/model/intent/train.py b/model/intent/train.py index 4bf70d7..9b147d6 100644 --- a/model/intent/train.py +++ b/model/intent/train.py @@ -185,6 +185,9 @@ def process_windows(windows): model.evaluate(X_test, y_test) print(classification_report(y_test_idxs, predictions)) + # Use the dark background style + plt.style.use('dark_background') + ## Plot history accuracy from model plt.plot(fit_history.history['loss']) plt.plot(fit_history.history['val_loss']) @@ -192,7 +195,45 @@ def process_windows(windows): plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'val'], loc='upper left') - plt.show() + plt.savefig('loss.png') + + from sklearn.preprocessing import StandardScaler + from sklearn.manifold import TSNE + import tensorflow as tf + + # Assuming `latent` has shape (samples, timesteps, channels, features) + seq = Sequential([ + expandalizer, + pretrained_encoder, + classifier.layers[0] + ]) + latent = seq(X_test) + + # Step 1: Reshape to 2D by flattening the last three dimensions + samples = latent.shape[0] # Number of samples + # Flatten the timesteps, channels, and features dimensions into a single dimension + latent_flat = tf.reshape(latent, (samples, -1)).numpy() + + # Step 2: Standardize the flattened data + scaler = StandardScaler() + X_scaled = scaler.fit_transform(latent_flat) + + # Step 3: Apply t-SNE + tsne = TSNE(n_components=2, perplexity=30, learning_rate=200, n_iter=1000) + X_tsne = tsne.fit_transform(X_scaled) + + # Step 5: Plot the t-SNE result + plt.figure(figsize=(10, 10)) + scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=i_test, cmap='viridis', alpha=0.7) + plt.colorbar(scatter, label='Labels') + plt.title('t-SNE Visualization of Labeled Data') + plt.xlabel('t-SNE Component 1') + plt.ylabel('t-SNE Component 2') + plt.grid(True) + + # Set the scatter plot aspect to be square + plt.axis('square') + plt.savefig('tsne.png') if __name__ == "__main__": From e3eb7707c9b11258f27d930565d87c2a1533d900 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Wed, 25 Sep 2024 10:16:09 -0500 Subject: 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YkCOOr#44R`5#8|rMyt}zLq+ZX54o2v4gdfE From d0ebd72c1102d1788b577310c76a5c0e58cf1328 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Thu, 26 Sep 2024 22:46:50 -0500 Subject: [PATCH 15/31] dilated expander and noise for classifier --- model/intent/model.py | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index 4d6062c..17a7e74 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -2,8 +2,8 @@ import keras from keras.models import Sequential -from keras.layers import Dense, Layer, Conv2D, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D, GlobalAveragePooling2D -from keras.layers import Activation, Multiply, BatchNormalization, Dropout, SpatialDropout1D +from keras.layers import Dense, Layer, Conv2D, DepthwiseConv2D, SeparableConv2D , Conv1D +from keras.layers import Activation, Multiply, BatchNormalization, Dropout, SpatialDropout1D, UpSampling2D, GlobalAveragePooling2D ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -116,21 +116,19 @@ def build(self, input_shape): ## First Layer to convert any channels to 64 ranged [0, 1] def create_first_layer(chs=64): return Sequential([ - Conv1D(chs//4, 3, padding='same'), + Conv1D(chs, 3, padding='same', dilation_rate=1), BatchNormalization(), Activation(act), - Conv1D(chs//2, 3, padding='same'), + Conv1D(chs, 3, padding='same', dilation_rate=2), BatchNormalization(), Activation(act), - Conv1D(chs//1, 3, padding='same'), - BatchNormalization(), Activation('sigmoid'), + Conv1D(chs, 3, padding='same', dilation_rate=4), + BatchNormalization(), + AddNoiseLayer(0.1), + Activation('relu'), ]) ## Last Layer to map latent space to custom classes def create_last_layer(classes): return Sequential([ - Sequential([ - GlobalAveragePooling2D(), Dense(16), - BatchNormalization(), Activation(act) - ]), - Dropout(0.2), + GlobalAveragePooling2D(), Dense(classes, activation='softmax', kernel_regularizer='l2') ]) \ No newline at end of file From fff110c9bd1b6984d9ca5b1ccd1837c8a1a32cbc Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Thu, 26 Sep 2024 23:16:05 -0500 Subject: [PATCH 16/31] causal padding for classifier dialated convs --- model/intent/model.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index 17a7e74..b43a476 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -3,7 +3,7 @@ from keras.models import Sequential from keras.layers import Dense, Layer, Conv2D, DepthwiseConv2D, SeparableConv2D , Conv1D -from keras.layers import Activation, Multiply, BatchNormalization, Dropout, SpatialDropout1D, UpSampling2D, GlobalAveragePooling2D +from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling2D, GlobalAveragePooling2D ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -116,14 +116,12 @@ def build(self, input_shape): ## First Layer to convert any channels to 64 ranged [0, 1] def create_first_layer(chs=64): return Sequential([ - Conv1D(chs, 3, padding='same', dilation_rate=1), + Conv1D(chs, 3, padding='causal', dilation_rate=1), BatchNormalization(), Activation(act), - Conv1D(chs, 3, padding='same', dilation_rate=2), + Conv1D(chs, 3, padding='causal', dilation_rate=2), BatchNormalization(), Activation(act), - Conv1D(chs, 3, padding='same', dilation_rate=4), - BatchNormalization(), - AddNoiseLayer(0.1), - Activation('relu'), + Conv1D(chs, 3, padding='causal', dilation_rate=4), + BatchNormalization(), AddNoiseLayer(0.1), Activation('relu'), ]) ## Last Layer to map latent space to custom classes From 7ac56845b2cf025a355b2236736345b3a9a9ef4e Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Mon, 30 Sep 2024 00:33:27 -0500 Subject: [PATCH 17/31] cleanup --- logic/ml_action.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logic/ml_action.py b/logic/ml_action.py index 8f127aa..e936602 100644 --- a/logic/ml_action.py +++ b/logic/ml_action.py @@ -6,7 +6,7 @@ from brainflow.board_shim import BoardShim # imported so decorator can recognize loaded model -from model.intent.model import SpatialAttention +import model.intent.model as model class MLAction(BaseLogic): def __init__(self, board, ema_decay=1/60): From 89b1765b8f2815eb18915bc5f2ceb190e85e7cb7 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Mon, 30 Sep 2024 16:32:48 -0500 Subject: [PATCH 18/31] back to conv1d switch back to conv1d, use standard scaler --- model/intent/autoencoder_reconstruct.png | Bin 311567 -> 330749 bytes model/intent/edf_train.py | 4 +- model/intent/model.py | 47 +++++++++++------------ model/intent/physionet_decoder.keras | Bin 102712 -> 130079 bytes model/intent/physionet_encoder.keras | Bin 94577 -> 166668 bytes 5 files changed, 24 insertions(+), 27 deletions(-) diff --git a/model/intent/autoencoder_reconstruct.png 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ztB;Ql>4qP#rh$pS|GcPZ3TEOpNIFVjFhf7ad8gxnZ*#8flhwGMo}S|4Gi?_tTtfVI zei90W8^CZbr+_#@=junIaH R1_>X~wK#rSs|afy`d{iSD`5Zt diff --git a/model/intent/edf_train.py b/model/intent/edf_train.py index 12f7095..cd7b83e 100644 --- a/model/intent/edf_train.py +++ b/model/intent/edf_train.py @@ -2,7 +2,7 @@ from keras.optimizers import Adam from keras.callbacks import EarlyStopping from sklearn.model_selection import train_test_split -from sklearn.preprocessing import MinMaxScaler as Scaler +from sklearn.preprocessing import StandardScaler as Scaler from model import auto_encoder @@ -25,7 +25,7 @@ # Build the autoencoder autoencoder = auto_encoder -autoencoder.compile(optimizer=Adam(learning_rate=0.01), loss='huber') +autoencoder.compile(optimizer=Adam(learning_rate=0.01), loss='mse') # Define the EarlyStopping callback early_stopping = EarlyStopping(monitor='val_loss', patience=2*4, restore_best_weights=True, verbose=0) diff --git a/model/intent/model.py b/model/intent/model.py index b43a476..0abe037 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -2,8 +2,8 @@ import keras from keras.models import Sequential -from keras.layers import Dense, Layer, Conv2D, DepthwiseConv2D, SeparableConv2D , Conv1D -from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling2D, GlobalAveragePooling2D +from keras.layers import Dense, Layer, DepthwiseConv1D, SeparableConv2D , Conv1D +from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -51,16 +51,16 @@ def call(self, inputs, training=None): return inputs + noise return inputs -kernel = (2, 3) -e_rates = [1, 2] +kernel = 3 +e_rates = [1, 2, 4] d_rates = list(reversed(e_rates)) act = 'elu' def create_inner_layer(filters, kernel, dilation_rates, end_stride=1): # dialated depthwise convolves followed by pointwise convolve return Sequential( - [DepthwiseConv2D(kernel, padding='same', dilation_rate=dr) for dr in dilation_rates] + - [Conv2D(filters, 1, padding='same', strides=end_stride)] + [DepthwiseConv1D(kernel, padding='same', dilation_rate=dr) for dr in dilation_rates] + + [Conv1D(filters, 1, padding='same', strides=end_stride)] ) @keras.utils.register_keras_serializable() @@ -68,7 +68,7 @@ class Block(Layer): def __init__(self, filters, kernel, dilation_rates, strides=1, **kwargs): super(Block, self).__init__(**kwargs) self.inner_layer = create_inner_layer(filters, kernel, dilation_rates, strides) - self.residual = Conv2D(filters, 1, padding='same', strides=strides) + self.residual = Conv1D(filters, 1, padding='same', strides=strides) def call(self, inputs): x = self.inner_layer(inputs) @@ -78,33 +78,30 @@ def call(self, inputs): def build(self, input_shape): super(Block, self).build(input_shape) - encoder = Sequential([ - ExpandDimsLayer(), - Block(16, kernel, e_rates, 2), - BatchNormalization(), Activation(act), # (80, 32, 16) + Block(64, kernel, e_rates, 2), + BatchNormalization(), Activation(act), # (80, 64) - Block(16, kernel, e_rates, 2), - BatchNormalization(), Activation(act), # (40, 16, 16) + Block(32, kernel, e_rates, 2), + BatchNormalization(), Activation(act), # (40, 32) - Block(16, kernel, e_rates, 2), - BatchNormalization(), Activation(act), # (20, 8, 16) + Block(32, kernel, e_rates, 2), + BatchNormalization(), Activation(act), # (20, 32) - create_inner_layer(16, kernel, e_rates), Activation(act) + create_inner_layer(16, kernel, e_rates), Activation(act) # (20, 16) ]) decoder = Sequential([ Block(16, kernel, d_rates), - BatchNormalization(), Activation(act), UpSampling2D(2), + BatchNormalization(), Activation(act), UpSampling1D(2), - Block(16, kernel, d_rates), - BatchNormalization(), Activation(act), UpSampling2D(2), + Block(32, kernel, d_rates), + BatchNormalization(), Activation(act), UpSampling1D(2), - Block(16, kernel, d_rates), - BatchNormalization(), Activation(act), UpSampling2D(2), + Block(32, kernel, d_rates), + BatchNormalization(), Activation(act), UpSampling1D(2), - create_inner_layer(1, kernel, d_rates), Activation('relu'), - SqueezeDimsLayer() + create_inner_layer(64, kernel, d_rates), Activation('linear') ]) auto_encoder = Sequential([ @@ -121,12 +118,12 @@ def create_first_layer(chs=64): Conv1D(chs, 3, padding='causal', dilation_rate=2), BatchNormalization(), Activation(act), Conv1D(chs, 3, padding='causal', dilation_rate=4), - BatchNormalization(), AddNoiseLayer(0.1), Activation('relu'), + BatchNormalization(), AddNoiseLayer(0.1), Activation('linear'), ]) ## Last Layer to map latent space to custom classes def create_last_layer(classes): return Sequential([ - GlobalAveragePooling2D(), + GlobalAveragePooling1D(), Dense(classes, activation='softmax', kernel_regularizer='l2') ]) \ No newline at end of file diff --git a/model/intent/physionet_decoder.keras b/model/intent/physionet_decoder.keras index 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z4ig^9bimWb-UG3-Ea{fnA-G4M7sbwNb+sjyP1bIb9I(*^@k-6&;z>Rbjq*`+n^G%X$`7|n(SSt9CU zY9;t=0Du92v?jFl_9?Z+iqS&2O)i>Jc_PaZl@Qjx;n>R*Y576I$y5hQjO2Z@bG1p4`MZ_Tw??xUqG-GY_= zs>$3sLYi~)^Bv|jyx5IZR#^AX$`vJKIVtc8e~JAoygyVSPTG`@pSy>*@fZ)U;hsT( Y#-0{VU6ra7)>e}K+bvCF{R>t5A1X(|KmY&$ From 5cae47858f1111c0fc000c197e4d1fbb402aaa90 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Tue, 1 Oct 2024 01:04:24 -0500 Subject: [PATCH 19/31] standard scaling for user data --- model/intent/model.py | 19 +++---------------- model/intent/pipeline.py | 4 ++++ 2 files changed, 7 insertions(+), 16 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index 0abe037..9c04a88 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -28,16 +28,6 @@ def call(self, inputs): ## Modification to follow along this paper ## https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-020-00203-9 -@keras.saving.register_keras_serializable() -class ExpandDimsLayer(Layer): - def call(self, inputs): - return tf.expand_dims(inputs, axis=-1) - -@keras.saving.register_keras_serializable() -class SqueezeDimsLayer(Layer): - def call(self, inputs): - return tf.squeeze(inputs, axis=-1) - # Noise Layer @keras.utils.register_keras_serializable() class AddNoiseLayer(Layer): @@ -113,12 +103,9 @@ def build(self, input_shape): ## First Layer to convert any channels to 64 ranged [0, 1] def create_first_layer(chs=64): return Sequential([ - Conv1D(chs, 3, padding='causal', dilation_rate=1), - BatchNormalization(), Activation(act), - Conv1D(chs, 3, padding='causal', dilation_rate=2), - BatchNormalization(), Activation(act), - Conv1D(chs, 3, padding='causal', dilation_rate=4), - BatchNormalization(), AddNoiseLayer(0.1), Activation('linear'), + Conv1D(chs, 3, padding='causal', dilation_rate=1), Activation(act), + Conv1D(chs, 3, padding='causal', dilation_rate=2), Activation(act), + Conv1D(chs, 3, padding='causal', dilation_rate=4), Activation('linear'), ]) ## Last Layer to map latent space to custom classes diff --git a/model/intent/pipeline.py b/model/intent/pipeline.py index f7162d4..0f7484e 100644 --- a/model/intent/pipeline.py +++ b/model/intent/pipeline.py @@ -5,8 +5,11 @@ from brainflow.data_filter import DataFilter, DetrendOperations, NoiseTypes, FilterTypes +from sklearn.preprocessing import StandardScaler as Scaler + abs_script_path = os.path.abspath(__file__) abs_script_dir = os.path.dirname(abs_script_path) +scaler = Scaler() ## preprocess and extract features to be shared between train and test def preprocess_data(session_data, sampling_rate): @@ -15,6 +18,7 @@ def preprocess_data(session_data, sampling_rate): DataFilter.remove_environmental_noise(session_data[eeg_chan], sampling_rate, NoiseTypes.FIFTY_AND_SIXTY.value) # bandpass to alpha, beta, gamma, 80 for resample effect mitigation DataFilter.perform_bandpass(session_data[eeg_chan], sampling_rate, 8, 80, 4, FilterTypes.BUTTERWORTH.value, 0) + session_data = scaler.fit_transform(session_data.reshape(-1, 1)).reshape(session_data.shape) return session_data def extract_features(preprocessed_data): From daccf909890253441197ea7cbe81d85ed32748f3 Mon Sep 17 00:00:00 2001 From: Chillout Charles <100386266+ChilloutCharles@users.noreply.github.com> Date: Tue, 1 Oct 2024 16:01:00 -0500 Subject: [PATCH 20/31] Reduce Training Patience --- model/intent/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model/intent/train.py b/model/intent/train.py index a5c709c..2e8b614 100644 --- a/model/intent/train.py +++ b/model/intent/train.py @@ -158,7 +158,7 @@ def process_windows(windows): model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy') ## Set up EarlyStopping - early_stopping = EarlyStopping(monitor='val_loss', patience=2*4, restore_best_weights=True, verbose=0) + early_stopping = EarlyStopping(monitor='val_loss', patience=2*2, restore_best_weights=True, verbose=0) ## Train the model batch_size = 128 @@ -238,4 +238,4 @@ def process_windows(windows): if __name__ == "__main__": main() - \ No newline at end of file + From 8d411e909580fc0b34f459f0047a5b98c1ddf180 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Tue, 1 Oct 2024 23:52:56 -0500 Subject: [PATCH 21/31] block change, perceptual loss, mse loss --- model/intent/autoencoder_reconstruct.png | Bin 330749 -> 355819 bytes model/intent/edf_train.py | 8 +- model/intent/model.py | 93 +++++++++++++++-------- model/intent/physionet_decoder.keras | Bin 130079 -> 121390 bytes model/intent/physionet_encoder.keras | Bin 166668 -> 156249 bytes 5 files changed, 64 insertions(+), 37 deletions(-) diff --git a/model/intent/autoencoder_reconstruct.png b/model/intent/autoencoder_reconstruct.png index 1e6981c42c8545e0eb3a6453c5b9c9aedc9d25ec..30ba5c6315aeeb162c2ada9b8b9b26ee53dc3274 100644 GIT binary patch literal 355819 zcmdSBc{G*r_dj}+;bc5A&rS--972X1g-V9Vl$lD(EMtZ^D6=FgV=9D@IV3Y>%se}W zkSQ~n?tc1xzMt=1>#lXz{r9ewW#REW@8`Yue(l%mwcmQCrJ+hi&Pt9#p{TB^UA~Dz 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a/model/intent/edf_train.py b/model/intent/edf_train.py index cd7b83e..f43e513 100644 --- a/model/intent/edf_train.py +++ b/model/intent/edf_train.py @@ -28,10 +28,10 @@ autoencoder.compile(optimizer=Adam(learning_rate=0.01), loss='mse') # Define the EarlyStopping callback -early_stopping = EarlyStopping(monitor='val_loss', patience=2*4, restore_best_weights=True, verbose=0) +early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True, verbose=0) # Train the autoencoder with early stopping -batch_size = 256 +batch_size = 256 * 2 epochs = 128 fit_history = autoencoder.fit( X_train, X_train, @@ -43,8 +43,8 @@ #Save the model print("Saving Model") -encoder = autoencoder.layers[-2] -decoder = autoencoder.layers[-1] +encoder = autoencoder.encoder +decoder = autoencoder.decoder encoder.save('physionet_encoder.keras') decoder.save('physionet_decoder.keras') diff --git a/model/intent/model.py b/model/intent/model.py index 9c04a88..23a65db 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -1,9 +1,10 @@ import tensorflow as tf import keras -from keras.models import Sequential +from keras.models import Sequential, Model from keras.layers import Dense, Layer, DepthwiseConv1D, SeparableConv2D , Conv1D from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D +from keras.losses import MeanSquaredError as MSE ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -41,68 +42,94 @@ def call(self, inputs, training=None): return inputs + noise return inputs -kernel = 3 +kernel = 2 e_rates = [1, 2, 4] d_rates = list(reversed(e_rates)) act = 'elu' -def create_inner_layer(filters, kernel, dilation_rates, end_stride=1): - # dialated depthwise convolves followed by pointwise convolve - return Sequential( - [DepthwiseConv1D(kernel, padding='same', dilation_rate=dr) for dr in dilation_rates] + - [Conv1D(filters, 1, padding='same', strides=end_stride)] - ) - @keras.utils.register_keras_serializable() -class Block(Layer): - def __init__(self, filters, kernel, dilation_rates, strides=1, **kwargs): - super(Block, self).__init__(**kwargs) - self.inner_layer = create_inner_layer(filters, kernel, dilation_rates, strides) - self.residual = Conv1D(filters, 1, padding='same', strides=strides) - +class ParallelDepthSeperableConv1D(Layer): + def __init__(self, filters, kernel_size, dilation_rates, stride=1, use_residual=False, **kwargs): + super(ParallelDepthSeperableConv1D, self).__init__(**kwargs) + self.filters = filters + self.dilation_rates = dilation_rates + self.depthwise_convs = [DepthwiseConv1D(kernel_size, padding='same', dilation_rate=dr) for dr in dilation_rates] + self.pointwise_conv = Conv1D(filters, 1, padding='same', strides=stride) + self.residual_conv = None + if use_residual: + self.residual_conv = Conv1D(filters, 1, padding='same', strides=stride) + def call(self, inputs): - x = self.inner_layer(inputs) - r = self.residual(inputs) - return x + r - + depthwise_outputs = [conv(inputs) for conv in self.depthwise_convs] + merged = tf.add_n(depthwise_outputs) + output = self.pointwise_conv(merged) + if self.residual_conv: + output += self.residual_conv(inputs) + return output + def build(self, input_shape): - super(Block, self).build(input_shape) + super(ParallelDepthSeperableConv1D, self).build(input_shape) encoder = Sequential([ - Block(64, kernel, e_rates, 2), + ParallelDepthSeperableConv1D(64, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (80, 64) - Block(32, kernel, e_rates, 2), + ParallelDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (40, 32) - Block(32, kernel, e_rates, 2), + ParallelDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (20, 32) - create_inner_layer(16, kernel, e_rates), Activation(act) # (20, 16) + ParallelDepthSeperableConv1D(16, kernel, e_rates, 1, False), # (20, 16) + Activation('linear') # for use in contrastive learning ]) decoder = Sequential([ - Block(16, kernel, d_rates), + ParallelDepthSeperableConv1D(16, kernel, e_rates, 1, True), BatchNormalization(), Activation(act), UpSampling1D(2), - Block(32, kernel, d_rates), + ParallelDepthSeperableConv1D(32, kernel, e_rates, 1, True), BatchNormalization(), Activation(act), UpSampling1D(2), - Block(32, kernel, d_rates), + ParallelDepthSeperableConv1D(32, kernel, e_rates, 1, True), BatchNormalization(), Activation(act), UpSampling1D(2), - create_inner_layer(64, kernel, d_rates), Activation('linear') + ParallelDepthSeperableConv1D(64, kernel, e_rates, 1, False), + Activation('linear') ]) -auto_encoder = Sequential([ - SpatialDropout1D(0.2), - encoder, - decoder -]) +## AutoEncoder Wrapper for edf_train +class CustomAutoencoder(Model): + def __init__(self, encoder, decoder, perceptual_weight=1.0, sd_rate=0.2): + super(CustomAutoencoder, self).__init__() + self.spatial_dropout = SpatialDropout1D(sd_rate) + self.encoder = encoder + self.decoder = decoder + self.perceptual_weight = perceptual_weight + self.mse_loss = MSE() + + def call(self, inputs): + # Encoding and reconstructing the input + inputs = self.spatial_dropout(inputs) + original_features = self.encoder(inputs) + reconstruction = self.decoder(original_features) + + # get features from reconstruction + reconstructed_features = self.encoder(reconstruction) + + # Compute and add perceptual loss during the call + perceptual_loss = self.mse_loss(original_features, reconstructed_features) + self.add_loss(self.perceptual_weight * perceptual_loss) + + # Return only the reconstruction for the main loss computation + return reconstruction + +auto_encoder = CustomAutoencoder(encoder, decoder) ## First Layer to convert any channels to 64 ranged [0, 1] def create_first_layer(chs=64): return Sequential([ + AddNoiseLayer(0.2), Conv1D(chs, 3, padding='causal', dilation_rate=1), Activation(act), Conv1D(chs, 3, 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zT;v7@W6tnC+)rnpxSk_d1}kFT?gG}gcrrTNX=c);X28Xxu4J>7236ohLXuKCxg3~E zqQ)5$nd3cKxhXTTj7_5H&dc$#eg@dL@IZg%e0Iw19Oh|67)q-~P*Zn9qM>02VGH%? z^s{DY9`8)vTU=-FI1S?LZ5s!-^S3b9olk*sT`|l^n~2Lgw_}bq7{H2o3=K1JB6+Ki zL-Dy!WcY>w9QP?zWYke3Y}}31J}rZjEGvu3@dL;X!*zIILJPbKk;Hx#XNxyA_N1L$ z3}OD{=Ws44;Wy_o>$(cGlCS6;LValQwBM)uO7Xw!<=^U+U;3ehd=sYsx!xG*&gZ6aW3#jj9~y%gX_NGeus> zBL%n>)+37J|FXKjY;mVyZQ6De?o>;f+v9qu>_sd7_UwX}di`CaNz}>?=z`f6$ z1#|sGiyo}0@pz*vCicsx!xu&8wB}~a^_w|+)^7?{hJjnRGdIu0dbJh%}HNa_@q|`5W#asV!Thk$Hf00!G2j_z_#sB~S From 32fa3273400a8b6d62f2a9aa567dde804be328b8 Mon Sep 17 00:00:00 2001 From: Chillout Charles <100386266+ChilloutCharles@users.noreply.github.com> Date: Wed, 2 Oct 2024 00:33:52 -0500 Subject: [PATCH 22/31] adding layer norms to final layer --- model/intent/model.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/model/intent/model.py b/model/intent/model.py index 23a65db..7c033b8 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -3,7 +3,7 @@ from keras.models import Sequential, Model from keras.layers import Dense, Layer, DepthwiseConv1D, SeparableConv2D , Conv1D -from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D +from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D, LayerNormalization from keras.losses import MeanSquaredError as MSE ## Spatial Attention (Thanks Summer!) @@ -130,9 +130,12 @@ def call(self, inputs): def create_first_layer(chs=64): return Sequential([ AddNoiseLayer(0.2), - Conv1D(chs, 3, padding='causal', dilation_rate=1), Activation(act), - Conv1D(chs, 3, padding='causal', dilation_rate=2), Activation(act), - Conv1D(chs, 3, padding='causal', dilation_rate=4), Activation('linear'), + Conv1D(chs, 3, padding='causal', dilation_rate=1), + LayerNormalization(), Activation(act), + Conv1D(chs, 3, padding='causal', dilation_rate=2), + LayerNormalization(), Activation(act), + Conv1D(chs, 3, padding='causal', dilation_rate=4), + LayerNormalization(), Activation('linear'), ]) ## Last Layer to map latent space to custom classes @@ -140,4 +143,4 @@ def create_last_layer(classes): return Sequential([ GlobalAveragePooling1D(), Dense(classes, activation='softmax', kernel_regularizer='l2') - ]) \ No newline at end of file + ]) From 80c97dea18eac69d94d5b097a6241756341d264d Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Wed, 2 Oct 2024 00:47:17 -0500 Subject: [PATCH 23/31] remove last layer norm --- model/intent/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model/intent/model.py b/model/intent/model.py index 7c033b8..523e5fa 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -135,7 +135,7 @@ def create_first_layer(chs=64): Conv1D(chs, 3, padding='causal', dilation_rate=2), LayerNormalization(), Activation(act), Conv1D(chs, 3, padding='causal', dilation_rate=4), - LayerNormalization(), Activation('linear'), + 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Attention +from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D, Input from keras.losses import MeanSquaredError as MSE, CategoricalCrossentropy, CosineSimilarity ## Spatial Attention (Thanks Summer!) @@ -90,10 +89,10 @@ def call(self, inputs): # activates LoRA layers for downstream def partial_trainable(seq_model): for layer in seq_model.layers: - if not isinstance(layer, LoRALayer): - layer.trainable = False + if isinstance(layer, LoRALayer): + layer.trainable = True else: - layer.trainable = True + layer.trainable = False ## Modification of seperable convolutions to follow along this paper ## https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-020-00203-9 @@ -120,18 +119,19 @@ def build(self, input_shape): super(StackedDepthSeperableConv1D, self).build(input_shape) encoder = Sequential([ + LoRALayer(rank), StackedDepthSeperableConv1D(64, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (80, 64) - LoRALayer(rank), + LoRALayer(rank), StackedDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (40, 32) - LoRALayer(rank), + LoRALayer(rank), StackedDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (20, 32) + LoRALayer(rank), - StackedDepthSeperableConv1D(16, kernel, e_rates, 1, False), # (20, 16) Activation('linear') ]) @@ -179,6 +179,7 @@ def call(self, inputs): auto_encoder = CustomAutoencoder(encoder, decoder) +## Classifier Model that trains for both classification and perceptual targets class PerceptualClassifier(Model): def __init__(self, encoder, decoder, classes, perceptual_weight=0.5, classify_weight=0.5, **kwargs): super(PerceptualClassifier, self).__init__(**kwargs) @@ -212,14 +213,14 @@ def get_loss_function(self): return lambda y_true, y_pred: self.classify_weight * self.cce_loss(y_true, y_pred) def get_lean_model(self): - inputs = Input(self.expander.input_shape[1:]) model = Sequential([ + Input(self.expander.input_shape[1:]), self.expander, self.encoder, self.classifier ]) - outputs = model(inputs) - return Model(inputs, outputs) + model.compile(optimizer='adam', loss='categorical_crossentropy') + return model # Custom Activation to maintain zero centered with max standard deviation of 3 def tanh3(x): @@ -239,5 +240,5 @@ def create_last_layer(classes): Sequential([ GlobalAveragePooling1D() ]), - Dense(classes, activation='softmax', kernel_regularizer='l2') + Dense(classes, activation='softmax') ]) diff --git a/model/intent/physionet_decoder.keras b/model/intent/physionet_decoder.keras index 2f47f1e6cf0f0b838ec82f7dbe0fdd5de6936e80..982b3f956ac7834803240ef582b8e8f7a2cf3aae 100644 GIT binary patch delta 28069 zcmd3t`Crb@7x&w=DbXg{Q;|@XC|z?VN|A^V*_R3xCHs1%U4@hutyB^fDI{HUCQ+1- zNJJ?S*^@2h(|vs(_rGxeaKHb9dC!?Suk(Dprt*VaU{Z$)22_EHhuEc%bQkf$$vjO%j2D{=e3dtS|Veia>I1~I%^c|j@U#~jNG}zcP%O} 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SAVE_FILENAME = "recorded_eeg" @@ -136,29 +135,24 @@ def process_windows(windows): X_test = np.concatenate([windows_test for _ , windows_test in processed_windows.values()]) y_test = to_categorical(i_test, num_classes=len(processed_windows)) - ## load pretrained encoder and keep it static + ## load pretrained encoder and keep it partially frozen pretrained_encoder = keras.models.load_model("physionet_encoder.keras") - pretrained_encoder.trainable = False + partial_trainable(pretrained_encoder) + ## load pretrained decoder freeze it for use in perceptual loss + pretrained_decoder = keras.models.load_model("physionet_decoder.keras") + pretrained_decoder.trainable = False - ## create channel expander/normalizer and classification layer + ## get class count from training data classes = len(processed_windows) - encoder_channels = pretrained_encoder.input_shape[-1] - expandalizer = create_first_layer(encoder_channels) - classifier = create_last_layer(classes) - ## Create Model - model = Sequential([ - expandalizer, - pretrained_encoder, - classifier - ]) + model = PerceptualClassifier(pretrained_encoder, pretrained_decoder, classes, 1, 1) ## Compile the model - model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy') + model.compile(optimizer=Adam(), loss=model.get_loss_function()) ## Set up EarlyStopping - early_stopping = EarlyStopping(monitor='val_loss', patience=2*2, restore_best_weights=True, verbose=0) + early_stopping = EarlyStopping(monitor='val_loss', patience=2*4, restore_best_weights=True, verbose=0) ## Train the model batch_size = 128 @@ -172,6 +166,7 @@ def process_windows(windows): ) ## Print out model summary + model = model.get_lean_model() model.summary() ## Save models for realtime use @@ -195,6 +190,7 @@ def process_windows(windows): plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'val'], loc='upper left') + plt.ylim(0, 1) plt.savefig('loss.png') from sklearn.preprocessing import StandardScaler @@ -202,12 +198,8 @@ def process_windows(windows): import tensorflow as tf # Assuming `latent` has shape (samples, timesteps, channels, features) - seq = Sequential([ - expandalizer, - pretrained_encoder, - classifier.layers[0] - ]) - latent = seq(X_test) + model.layers[-1] = model.layers[-1].layers[0] + latent = model(X_test) # Step 1: Reshape to 2D by flattening the last three dimensions samples = latent.shape[0] # Number of samples From ccf0245bb5b7a6d3ee094bc5a09a0ac28fb46365 Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Fri, 11 Oct 2024 20:08:25 -0500 Subject: [PATCH 26/31] student teacher model --- model/intent/autoencoder_reconstruct.png | Bin 334478 -> 300962 bytes model/intent/model.py | 111 ++++++----------------- model/intent/physionet_decoder.keras | Bin 128322 -> 141634 bytes model/intent/physionet_encoder.keras | Bin 187498 -> 167599 bytes model/intent/train.py | 4 +- 5 files changed, 32 insertions(+), 83 deletions(-) diff --git a/model/intent/autoencoder_reconstruct.png 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UpSampling1D, GlobalAveragePooling1D, Input +from keras.layers import Activation, Multiply, BatchNormalization, SpatialDropout1D, UpSampling1D, GlobalAveragePooling1D, Input, Dropout from keras.losses import MeanSquaredError as MSE, CategoricalCrossentropy, CosineSimilarity ## Spatial Attention (Thanks Summer!) @@ -55,44 +55,10 @@ def call(self, inputs, training=None): ## https://www.physionet.org/content/eegmmidb/1.0.0/ ## Thanks again to Summer, Programmerboi, Hosomi -kernel = 2 +kernel = 3 e_rates = [1, 2, 4] d_rates = list(reversed(e_rates)) act = 'elu' -rank = 8 # Low-rank dimension for LoRA - -## LoRA layers for use in downstream classification -## https://arxiv.org/abs/2106.09685 -@keras.utils.register_keras_serializable() -class LoRALayer(Layer): - def __init__(self, rank, **kwargs): - super(LoRALayer, self).__init__(**kwargs) - self.rank = rank - - def build(self, input_shape): - input_dim = input_shape[-1] - self.A = self.add_weight(shape=(input_dim, self.rank), - initializer='random_normal', - trainable=True, - name='lora_A') - self.B = self.add_weight(shape=(self.rank, input_dim), - initializer='random_normal', - trainable=True, - name='lora_B') - super(LoRALayer, self).build(input_shape) - - def call(self, inputs): - lora_update = tf.matmul(inputs, self.A) - lora_update = tf.matmul(lora_update, self.B) - return inputs + lora_update - -# activates LoRA layers for downstream -def partial_trainable(seq_model): - for layer in seq_model.layers: - if isinstance(layer, LoRALayer): - layer.trainable = True - else: - layer.trainable = False ## Modification of seperable convolutions to follow along this paper ## https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-020-00203-9 @@ -119,25 +85,21 @@ def build(self, input_shape): super(StackedDepthSeperableConv1D, self).build(input_shape) encoder = Sequential([ - LoRALayer(rank), StackedDepthSeperableConv1D(64, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (80, 64) - LoRALayer(rank), StackedDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (40, 32) - LoRALayer(rank), StackedDepthSeperableConv1D(32, kernel, e_rates, 2, True), BatchNormalization(), Activation(act), # (20, 32) - - LoRALayer(rank), - StackedDepthSeperableConv1D(16, kernel, e_rates, 1, False), # (20, 16) + + StackedDepthSeperableConv1D(32, kernel, e_rates, 1, False), Activation('linear') ]) decoder = Sequential([ - StackedDepthSeperableConv1D(16, kernel, e_rates, 1, True), + StackedDepthSeperableConv1D(32, kernel, e_rates, 1, True), BatchNormalization(), Activation(act), UpSampling1D(2), StackedDepthSeperableConv1D(32, kernel, e_rates, 1, True), @@ -181,29 +143,38 @@ def call(self, inputs): ## Classifier Model that trains for both classification and perceptual targets class PerceptualClassifier(Model): - def __init__(self, encoder, decoder, classes, perceptual_weight=0.5, classify_weight=0.5, **kwargs): + def __init__(self, frozen_encoder, frozen_decoder, classes, perceptual_weight=1.0, classify_weight=1.0, **kwargs): super(PerceptualClassifier, self).__init__(**kwargs) - self.encoder = encoder - self.decoder = decoder - - channels = self.encoder.input_shape[-1] - self.expander = create_first_layer(channels) - self.classifier = create_last_layer(classes) + # create teacher from frozen models + self.teacher = Sequential([frozen_decoder, frozen_encoder]) + + # create student from pieces of unfrozen encoder + first_layer = [StackedDepthSeperableConv1D(64, kernel, e_rates, 2, True)] + cloned_encoder = clone_model(frozen_encoder) + cloned_encoder.set_weights(cloned_encoder.get_weights()) + encoder_layers = first_layer + cloned_encoder.layers[1:] + self.student = Sequential(encoder_layers) + + # classifier + self.classifier = Sequential([ + GlobalAveragePooling1D(), + Dense(classes, activation='softmax', kernel_regularizer='l2') + ]) + # perceptual and classification losses self.perceptual_weight = perceptual_weight self.classify_weight = classify_weight - self.percept_loss = lambda y_true, y_pred: 1 + CosineSimilarity(axis=-1)(y_true, y_pred) + self.percept_loss = MSE() self.cce_loss = CategoricalCrossentropy() def call(self, inputs): - expand = self.expander(inputs) - features = self.encoder(expand) + # predict class + features = self.student(inputs) output = self.classifier(features) - reconstruct = self.decoder(features) - reconstruct_features = self.encoder(reconstruct) - + # teach + reconstruct_features = self.teacher(features) perceptual_loss = self.perceptual_weight * self.percept_loss(features, reconstruct_features) self.add_loss(perceptual_loss) @@ -214,31 +185,9 @@ def get_loss_function(self): def get_lean_model(self): model = Sequential([ - Input(self.expander.input_shape[1:]), - self.expander, - self.encoder, + Input(self.student.input_shape[1:]), + self.student, self.classifier ]) model.compile(optimizer='adam', loss='categorical_crossentropy') return model - -# Custom Activation to maintain zero centered with max standard deviation of 3 -def tanh3(x): - return tf.nn.tanh(x) * 3.5 -keras.utils.get_custom_objects().update({'tanh3': tanh3}) - -## First Layer to convert any channels to 64, standard scaled -def create_first_layer(chs=64): - return Sequential([ - AddNoiseLayer(0.2), - Conv1D(chs, 1, padding='same', use_bias=False, activation=tanh3) - ]) - -## Last Layer to map latent space to custom classes -def create_last_layer(classes): - return Sequential([ - Sequential([ - GlobalAveragePooling1D() - ]), - Dense(classes, activation='softmax') - ]) diff --git a/model/intent/physionet_decoder.keras b/model/intent/physionet_decoder.keras index 982b3f956ac7834803240ef582b8e8f7a2cf3aae..f51b6cc207d99f0babd7dd697f4ab143d883219a 100644 GIT binary patch literal 141634 zcmeEv30#d^_kV*Fp$sX}AXA|fDb?9)XG-SGvx-WQG)ty5kdiXbDv1bXO6u&jiIhy4 zXF`&Bo~QrGInTX6=f19c-@kj``@TATI_vEHtY@#izt7rh*!wxpaq6jDrJjQH)mUNj z314+8DOCK|l>AS04|H{N4RkddA27*hs?h{@f7bx#V0ZrjuSq^ewuVOS%p`wCtqqMN 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= "recorded_eeg" @@ -137,7 +137,7 @@ def process_windows(windows): ## load pretrained encoder and keep it partially frozen pretrained_encoder = keras.models.load_model("physionet_encoder.keras") - partial_trainable(pretrained_encoder) + pretrained_encoder.trainable = False ## load pretrained decoder freeze it for use in perceptual loss pretrained_decoder = keras.models.load_model("physionet_decoder.keras") pretrained_decoder.trainable = False From 5a4e59ee1e859b09d4d79c6942dca9997b62165f Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Fri, 11 Oct 2024 20:46:23 -0500 Subject: [PATCH 27/31] decoder fix --- model/intent/autoencoder_reconstruct.png | Bin 300962 -> 340026 bytes model/intent/model.py | 8 ++++---- model/intent/physionet_decoder.keras | Bin 141634 -> 141634 bytes model/intent/physionet_encoder.keras | Bin 167599 -> 167599 bytes 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/model/intent/autoencoder_reconstruct.png b/model/intent/autoencoder_reconstruct.png 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zVQR>VFO?<-UfjWQZRs>8GL$)T!hLLNPqwVU?fI2t{r7*6;ei|~==-#ap=`3Q3V5A_#+2LJ#7 From 23ae513f430e508b661503532a018866a8c0af4e Mon Sep 17 00:00:00 2001 From: Chillout Charles Date: Wed, 25 Sep 2024 10:24:24 -0500 Subject: [PATCH 11/31] Added Dropout Back --- model/intent/model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/model/intent/model.py b/model/intent/model.py index 2e30c15..ee1064c 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -3,7 +3,7 @@ from keras.models import Sequential from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D, GlobalAveragePooling2D -from keras.layers import Activation, Multiply, BatchNormalization +from keras.layers import Activation, Multiply, BatchNormalization, Dropout ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() @@ -115,5 +115,6 @@ def create_last_layer(classes): GlobalAveragePooling2D(), Dense(16), BatchNormalization(), Activation(act) ]), 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patience=2*4, restore_best_weights=True, verbose=0) # Train the autoencoder with early stopping batch_size = 256 diff --git a/model/intent/model.py b/model/intent/model.py index aaa9cc7..de1a412 100644 --- a/model/intent/model.py +++ b/model/intent/model.py @@ -2,18 +2,15 @@ import keras from keras.models import Sequential -from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D +from keras.layers import Dense, Layer, DepthwiseConv2D, SeparableConv2D , Conv1D, UpSampling2D, MaxPooling2D from keras.layers import Activation, Flatten, Multiply, BatchNormalization, Dropout ## Spatial Attention (Thanks Summer!) @keras.saving.register_keras_serializable() class SpatialAttention(Layer): - def __init__(self, classes, kernel_size=7, **kwargs): + def __init__(self, kernel_size=7, **kwargs): super(SpatialAttention, self).__init__(**kwargs) - self.kernel_size = kernel_size - self.classes = classes - self.conv1 = Conv1D(self.classes, self.kernel_size, padding='same', activation='silu', use_bias=False) - self.conv2 = Conv1D(1, self.kernel_size, padding='same', activation='sigmoid', use_bias=False) + self.attn = SeparableConv2D(1, kernel_size, padding='same', activation='sigmoid') def build(self, input_shape): super(SpatialAttention, self).build(input_shape) @@ -21,16 +18,15 @@ def build(self, input_shape): def call(self, inputs): avg_out = tf.reduce_mean(inputs, axis=-1, keepdims=True) max_out = tf.reduce_max(inputs, axis=-1, keepdims=True) - x = tf.concat([avg_out, max_out], axis=2) - x = self.conv1(x) - x = self.conv2(x) + x = tf.concat([avg_out, max_out], axis=-1) + x = self.attn(x) return Multiply()([inputs, x]) ## Encoder and Decoder Trained on the physionet motor imagery dataset ## https://www.physionet.org/content/eegmmidb/1.0.0/ ## Thanks again to Summer, Programmerboi, Hosomi ## Modification to follow along this paper -## https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522466/ +## https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-020-00203-9 @keras.saving.register_keras_serializable() class ExpandDimsLayer(Layer): @@ -43,36 +39,42 @@ def call(self, inputs): return tf.squeeze(inputs, axis=-1) kernel = (3, 3) +e_rates = [1, 2] +d_rates = list(reversed(e_rates)) act = 'leaky_relu' +def create_block(filters, kernel, dilation_rates, end_stride=1): + # dialated depthwise convolves followed by pointwise convolve + return Sequential( + [DepthwiseConv2D(kernel, padding='same', dilation_rate=dr) for dr in dilation_rates] + + [SeparableConv2D(filters, 1, padding='same', strides=end_stride)] + ) + encoder = Sequential([ ExpandDimsLayer(), - SeparableConv2D(16, kernel, padding='same'), - DepthwiseConv2D(kernel, padding='same', strides=2), + create_block(16, kernel, e_rates, 2), BatchNormalization(), Activation(act), - SeparableConv2D(16, kernel, padding='same'), - DepthwiseConv2D(kernel, padding='same', strides=2), + create_block(16, kernel, e_rates, 2), BatchNormalization(), Activation(act), - - SeparableConv2D(16, kernel, padding='same'), - DepthwiseConv2D(kernel, padding='same', strides=2), + + create_block(16, kernel, e_rates, 2), BatchNormalization(), Activation(act), - SeparableConv2D(16, kernel, padding='same'), + create_block(16, kernel, e_rates), Activation(act) ]) decoder = Sequential([ - SeparableConv2D(16, kernel, padding='same'), + create_block(16, kernel, d_rates), BatchNormalization(), Activation(act), UpSampling2D(2), - SeparableConv2D(16, kernel, padding='same'), + create_block(16, kernel, d_rates), BatchNormalization(), Activation(act), UpSampling2D(2), - SeparableConv2D(16, kernel, padding='same'), + create_block(16, kernel, d_rates), BatchNormalization(), Activation(act), UpSampling2D(2), - SeparableConv2D(1, kernel, padding='same'), Activation('relu'), + create_block(1, kernel, d_rates), Activation('relu'), SqueezeDimsLayer() ]) diff --git a/model/intent/physionet_decoder.keras 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