Create test.py

test.py

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+# Auto-encoder to extract features from DNA Methylation and CNV data
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
+from tensorflow.keras import regularizers
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
+
+with tf.device('/gpu:0'):
+    # this is the size of our encoded representations
+    encoding_dim1 = 500
+    encoding_dim2 = 200
+
+    lambda_act = 0.0001
+    lambda_weight = 0.001
+    # this is our input placeholder
+    input_data = Input(shape=(num_in_neurons,))
+    # first encoded representation of the input
+    encoded = Dense(encoding_dim1, activation='relu', activity_regularizer=regularizers.l1(lambda_act), kernel_regularizer=regularizers.l2(lambda_weight), name='encoder1')(input_data)
+    encoded = BatchNormalization()(encoded)
+    encoded = Dropout(0.5)(encoded)
+
+    # second encoded representation of the input
+
+    encoded = Dense(encoding_dim2, activation='relu', activity_regularizer=regularizers.l1(lambda_act), kernel_regularizer=regularizers.l2(lambda_weight), name='encoder2')(encoded)
+    encoded = BatchNormalization()(encoded)
+    encoded = Dropout(0.5)(encoded)
+
+    # first lossy reconstruction of the input
+
+    decoded = Dense(encoding_dim1, activation='relu', name='decoder1')(encoded)
+    decoded = BatchNormalization()(decoded)
+
+    # the final lossy reconstruction of the input
+    decoded = Dense(num_in_neurons, activation='sigmoid', name='decoder2')(decoded)
+
+    # this model maps an input to its reconstruction
+    autoencoder = Model(inputs=input_data, outputs=decoded)
+
+    myencoder = Model(inputs=input_data, outputs=encoded)
+    autoencoder.compile(optimizer=Adam(), loss='mse')
+      # setup callbacks
+    callbacks = [
+        EarlyStopping(monitor='val_loss', patience=5, verbose=1),
+        ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True, verbose=1)
+    ]
+    # training
+    print('training the autoencoder')
+    autoencoder.fit(x_train_noisy, x_train,
+                    epochs=25,
+                    batch_size=8,
+                    shuffle=True,
+                    validation_data=(x_test_noisy, x_test),
+                    callbacks=callbacks)
+    autoencoder.trainable = False   #freeze autoencoder weights