如何在Keras中获得手动运行会话的可训练权重

Question

因为我正在手动运行一个会话，所以我似乎无法收集特定层的可训练权重。

    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))

我试图得到最后一个卷积层的权重。

我尝试了get_trainable_weights(layer)和layer.get_weights()，但我没有设法得到任何地方。

误差

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

Answer 1

从查看源代码*，似乎你正在寻找层。Trainable_weights(它是一个列表而不是成员函数)。请注意，这个返回张量。

如果你想获得它们的实际值，你需要在会话中计算它们:

weights1, weights2 = sess.run([weight_tensor_1, weight_tensor_2])

* https://github.com/fchollet/keras/blob/master/keras/layers/convolutional.py L401