Update test.py

test.py

@@ -1,4 +1,3 @@
-# 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
@@ -10,46 +9,60 @@ 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 = Dense(encoding_dim1, activation='relu', 
+                    activity_regularizer=regularizers.l1(lambda_act),
+                    kernel_regularizer=regularizers.l2(lambda_weight))(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 = Dense(encoding_dim2, activation='relu', 
+                    activity_regularizer=regularizers.l1(lambda_act),
+                    kernel_regularizer=regularizers.l2(lambda_weight))(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 = Dense(encoding_dim1, activation='relu')(encoded)
     decoded = BatchNormalization()(decoded)
-
+    
     # the final lossy reconstruction of the input
-    decoded = Dense(num_in_neurons, activation='sigmoid', name='decoder2')(decoded)
-
+    decoded = Dense(num_in_neurons, activation='sigmoid')(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
+    
+    # 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')
+    print('Training the autoencoder')
     autoencoder.fit(x_train_noisy, x_train,
-                    epochs=25,
-                    batch_size=8,
+                    epochs=50,
+                    batch_size=16,
                     shuffle=True,
                     validation_data=(x_test_noisy, x_test),
                     callbacks=callbacks)
-    autoencoder.trainable = False   #freeze autoencoder weights
+
+    # Load best model
+    autoencoder.load_weights('best_model.h5')
+
+    # Freeze the weights
+    autoencoder.trainable = False
+
+    # Use the trained encoder for predictions
+    myencoder = Model(inputs=input_data, outputs=encoded)
+    ae_train = myencoder.predict(x_train)
+    ae_test = myencoder.predict(x_test)