shouldn't model.trainable=False freeze weights under the model?

I am trying to freeze the free trained VGG16’s layers (‘conv_base’ below) and add new layers on top of them for feature extracting. I expect to get same prediction results from ‘conv_base’ before(ret1) / after(ret2) fit of model but it is not. Is this wrong way to check weight freezing?

# loading VGG16 and set to untrainable conv_base = applications.VGG16(weights='imagenet', include_top=False, input_shape=[150, 150, 3]) conv_base.trainable = False

#result before model fit ret1 = conv_base.predict(np.ones([1, 150, 150, 3]))

# add layers on top of the VGG16 and compile a model model = models.Sequential() model .add(conv_base) model .add(layers.Flatten()) model .add(layers.Dense(10, activation='relu')) model .add(layers.Dense(1, activation='sigmoid')) model.compile('rmsprop', 'binary_crossentropy', ['accuracy'])

# fit the model model.fit_generator(train_generator, 100, validation_data=validation_generator, validation_steps=50)

#result after model fit ret2 = conv_base.predict(np.ones([1, 150, 150, 3]))

#hope this is True but it is not. np.equal(ret1, ret2)

[fchollet]

I fixed it: https://github.com/fchollet/keras/commit/c25fa38deb4efc5445f64af3ec17eae0eb660d2f

[anujgupta82]

if you set model. trainable = False, should it not make layer.trainable for all layers false?

conv_base_model = VGG16(weights='imagenet', input_shape=(150, 150, 3), include_top=False)

conv_base_model.trainable = False

for layer in conv_base_model.layers:
    print(layer.name, layer.trainable)

I am still getting true for all layers.

Am I missing something?