Updates

Models/model_lenet5.py

@@ -94,10 +94,10 @@ model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'
 history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[tensorboard,checkpointer,early_stopping])
 
 # plot the learning curves
-pyplot.plot(history.history['accuracy'],label='train')
-pyplot.plot(history.history['val_accuracy'],label='validation')
-pyplot.legend()
-pyplot.show()
+# pyplot.plot(history.history['accuracy'],label='train')
+# pyplot.plot(history.history['val_accuracy'],label='validation')
+# pyplot.legend()
+# pyplot.show()
 
 
 

Models/model_sk_vgg16.py

@@ -58,6 +58,7 @@ base_filters=32
 learning_rate=.00001
 batch_size=256
 epochs=200
+patience_on_early_stop=5
 
 # Create a TensorBoard instance with the path to the logs directory.  Before training, in Terminal, run tensorboard --logdir=logs/
 tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
@@ -156,26 +157,21 @@ model.summary()
 # File path to save the file.  Only save the weights if there is an improvement.
 checkpointer=ModelCheckpoint(filepath=model_weights_file,verbose=1,save_best_only=True)
 
-#earlystopping=EarlyStopping(monitor='val_loss',patience=5)
+#Early Stopping
+early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=patience_on_early_stop)
 
 # Adam optimizer with learning rate .0001
-#optimizer=adam_v2.Adam(learning_rate=10e-6)
-#optimizer=adam_v2.Adam(learning_rate=10e-6)
-#optimizer=adam_v2.Adam(learning_rate=learning_rate)
 #Optimizer
 optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
 
 # Cross entropy loss function
-#model.compile(loss='categorical_crossentropy', optimizer=optimizer,metrics=['accuracy'])
-#model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])
 model.compile(loss='binary_crossentropy', optimizer=optimizer,metrics=['accuracy'])
-#model.compile(loss='binary_crossentropy', optimizer=optimizer,metrics=['accuracy'])
 
 # Allows to do real-time data augmentation on images on cpu in parallel to training your model on gpu.  
 # The callback to checkpointer saves the model wights.  Other callback can be added...like a stopping function
 # history=model.fit_generator(dataGen.flow(x_train,y_train,batch_size=batch_size),callbacks=[checkpointer],steps_per_epoch=x_train.shape[0] //
 # batch_size,epochs=epochs,verbose=2,validation_data=(x_valid,y_valid))
-history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[tensorboard,checkpointer])
+history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[earlystopping,tensorboard,checkpointer])
 
 # Here is the evaluation part
 # score=model.evaluate(x=train_ds,verbose=1)
@@ -200,16 +196,3 @@ history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=bat
 # pyplot.plot(history.history['val_accuracy'],label='validation')
 # pyplot.legend()
 # pyplot.show()
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