根据这个线程的所有好答案，我写了一个库来获取每一层的输出。它抽象了所有的复杂性，并被设计成尽可能友好的用户:

https://github.com/philipperemy/keract

它可以处理几乎所有的边界情况。

希望能有所帮助!

希望将此作为评论(但没有足够高的代表)添加到@indraforyou的回答中，以纠正@mathtick的评论中提到的问题。为了避免InvalidArgumentError: input_X:Y被提供和获取。异常，只需替换行输出=[层。模型中层的输出。输出= [layer.]模型中层的输出。层][1:]。

调整indraforyou的最小工作示例:

from keras import backend as K 
inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers][1:]        # all layer outputs except first (input) layer
functor = K.function([inp, K.learning_phase()], outputs )   # evaluation function

# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = functor([test, 1.])
print layer_outs

附注:我尝试输出=[层。模型中层的输出。Layers[1:]]不起作用。

好吧，其他答案都很完整，但有一个非常基本的方法来“看到”，而不是“得到”形状。

只需执行一个model.summary()。它将打印所有图层及其输出形状。“None”值将指示可变维度，第一个维度将是批处理大小。

如果你有以下情况之一:

InvalidArgumentError: input_X:Y既被提供也被获取 多输入情况

您需要做以下更改:

为输出变量中的输入层添加过滤 函子循环的微小变化

最小的例子:

from keras.engine.input_layer import InputLayer
inp = model.input
outputs = [layer.output for layer in model.layers if not isinstance(layer, InputLayer)]
functors = [K.function(inp + [K.learning_phase()], [x]) for x in outputs]
layer_outputs = [fun([x1, x2, xn, 1]) for fun in functors]

通常，输出大小可以计算为

[(W−K + 2P / S] + 1

在哪里

W is the input volume - in your case you have not given us this
K is the Kernel size - in your case 2 == "filter" 
P is the padding - in your case 2
S is the stride - in your case 3

另一个更漂亮的说法是:

假设你有:

1-刻苦训练前模型。

2-输入x作为图像或图像集。图像的分辨率应与输入层的尺寸相适应。例如，对于3通道(RGB)图像，80*80*3。

3-获得激活的输出层的名称。例如，“flat_2”图层。这应该包括在layer_names变量中，表示给定模型的层名。

4- batch_size为可选参数。

然后你可以很容易地使用get_activation函数来获得给定输入x和预训练模型的输出层的激活:

import six
import numpy as np
import keras.backend as k
from numpy import float32
def get_activations(x, model, layer, batch_size=128):
"""
Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
`nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
calling `layer_names`.
:param x: Input for computing the activations.
:type x: `np.ndarray`. Example: x.shape = (80, 80, 3)
:param model: pre-trained Keras model. Including weights.
:type model: keras.engine.sequential.Sequential. Example: model.input_shape = (None, 80, 80, 3)
:param layer: Layer for computing the activations
:type layer: `int` or `str`. Example: layer = 'flatten_2'
:param batch_size: Size of batches.
:type batch_size: `int`
:return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
:rtype: `np.ndarray`. Example: activations.shape = (1, 2000)
"""

    layer_names = [layer.name for layer in model.layers]
    if isinstance(layer, six.string_types):
        if layer not in layer_names:
            raise ValueError('Layer name %s is not part of the graph.' % layer)
        layer_name = layer
    elif isinstance(layer, int):
        if layer < 0 or layer >= len(layer_names):
            raise ValueError('Layer index %d is outside of range (0 to %d included).'
                             % (layer, len(layer_names) - 1))
        layer_name = layer_names[layer]
    else:
        raise TypeError('Layer must be of type `str` or `int`.')

    layer_output = model.get_layer(layer_name).output
    layer_input = model.input
    output_func = k.function([layer_input], [layer_output])

    # Apply preprocessing
    if x.shape == k.int_shape(model.input)[1:]:
        x_preproc = np.expand_dims(x, 0)
    else:
        x_preproc = x
    assert len(x_preproc.shape) == 4

    # Determine shape of expected output and prepare array
    output_shape = output_func([x_preproc[0][None, ...]])[0].shape
    activations = np.zeros((x_preproc.shape[0],) + output_shape[1:], dtype=float32)

    # Get activations with batching
    for batch_index in range(int(np.ceil(x_preproc.shape[0] / float(batch_size)))):
        begin, end = batch_index * batch_size, min((batch_index + 1) * batch_size, x_preproc.shape[0])
        activations[begin:end] = output_func([x_preproc[begin:end]])[0]

    return activations