How to get trainable weights?

Because I’m manually running a session, I can’t seem to collect the trainable weights of a specific layer.

# Keras layers can be called on TensorFlow tensors:
        x = Convolution2D(16, 3, 3, init='he_normal', border_mode='same')(img)

        for i in range(0, self.blocks_per_group):
            nb_filters = 16 * self.widening_factor
            x = residual_block(x, nb_filters=nb_filters, subsample_factor=1)

        for i in range(0, self.blocks_per_group):
            nb_filters = 32 * self.widening_factor
            if i == 0:
                subsample_factor = 2
            else:
                subsample_factor = 1
            x = residual_block(x, nb_filters=nb_filters, subsample_factor=subsample_factor)

        for i in range(0, self.blocks_per_group):
            nb_filters = 64 * self.widening_factor
            if i == 0:
                subsample_factor = 2
            else:
                subsample_factor = 1
            x = residual_block(x, nb_filters=nb_filters, subsample_factor=subsample_factor)

        x = BatchNormalization(axis=3)(x)
        x = Activation('relu')(x)
        x = AveragePooling2D(pool_size=(8, 8), strides=None, border_mode='valid')(x)
        x = tf.reshape(x, [-1, np.prod(x.get_shape()[1:].as_list())])

        # Readout layer
        preds = Dense(self.nb_classes, activation='softmax')(x)

       loss = tf.reduce_mean(categorical_crossentropy(labels, preds))

        optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

        with sess.as_default():

            for i in range(10):

                batch = self.next_batch(self.batch_num)
                _, l = sess.run([optimizer, loss],
                                feed_dict={img: batch[0], labels: batch[1]})
                print(l)
                print(type(weights))

I’m trying to get the weights of the last convolution layer.

I tried get_trainable_weights(layer) and layer.get_weights()but I did not manage to get anywhere.

The error

AttributeError: 'Tensor' object has no attribute 'trainable_weights'

[ritchieng]

@fchollet If I were to follow your guide and integrate with my TensorFlow workflow (https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html) as with others, you cannot access the weight variable because we won’t be building the model as shown in your guide. We’re merely using the layers. There is no need to compile when we use it as a simplified interface to TensorFlow. How then do we access the weights?

Because if we use with TensorFlow like the guide, we do not call Model or Compile but merely use the layers to build.

[fchollet]

model.trainable_weights is the list of trainable weights of a model. Of course you should first define a model in that case.

You can also retrieve that attribute separately on every layer (layer.trainable_weights).