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Sean
2024-10-27 09:16:28 -04:00
parent 9db3b0bcd9
commit 04daecbd44
2 changed files with 8 additions and 25 deletions
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8
Models/model_lenet5.py
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@@ -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]) history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[tensorboard,checkpointer,early_stopping])
# plot the learning curves # plot the learning curves
pyplot.plot(history.history['accuracy'],label='train') # pyplot.plot(history.history['accuracy'],label='train')
pyplot.plot(history.history['val_accuracy'],label='validation') # pyplot.plot(history.history['val_accuracy'],label='validation')
pyplot.legend() # pyplot.legend()
pyplot.show() # pyplot.show()

25
Models/model_sk_vgg16.py
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@@ -58,6 +58,7 @@ base_filters=32
learning_rate=.00001 learning_rate=.00001
batch_size=256 batch_size=256
epochs=200 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/ # 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())) 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. # 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) 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 # 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
optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999,epsilon=1e-8) optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
# Cross entropy loss function # 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'])
#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. # 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 # 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] // # 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)) # 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 # Here is the evaluation part
# score=model.evaluate(x=train_ds,verbose=1) # 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.plot(history.history['val_accuracy'],label='validation')
# pyplot.legend() # pyplot.legend()
# pyplot.show() # pyplot.show()