Crax Rat _best_ -

# Training history = model.fit(train_generator, steps_per_epoch=train_generator.samples // 32, validation_data=validation_generator, validation_steps=validation_generator.samples // 32, epochs=10)

# Data augmentation train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) crax rat

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Training history = model

model = Model(inputs=base_model.input, outputs=predictions) # Training history = model.fit(train_generator

x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x)