x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x)
train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=32, class_mode='categorical') crax rat
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) x = base_model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) activation='relu')(x) predictions = Dense(1
