你可以通过使用:model.layers[index].output轻松获得任何层的输出

对于所有层使用这个:

from keras import backend as K

inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers]          # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs]    # evaluation functions

# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test, 1.]) for func in functors]
print layer_outs

注意:要模拟Dropout，请使用learning_phase作为1。在layer_outs中，否则使用0。

编辑:(根据评论)

K.function创建了ano/tensorflow张量函数，之后用于从给定输入的符号图中获得输出。

现在需要K.learning_phase()作为输入，因为许多Keras层(如Dropout/Batchnomalization)依赖它来改变训练和测试期间的行为。

所以如果你在你的代码中删除dropout层，你可以简单地使用:

from keras import backend as K

inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers]          # all layer outputs
functors = [K.function([inp], [out]) for out in outputs]    # evaluation functions

# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test]) for func in functors]
print layer_outs

编辑2:更优化

我刚刚意识到，前面的答案并没有优化，因为对于每个函数的计算，数据将被CPU->GPU内存传输，而且张量计算需要对较低的层进行n-over。

相反，这是一种更好的方式，因为你不需要多个函数，而是一个函数给你所有输出的列表:

from keras import backend as K

inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers]          # all layer outputs
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

我为自己写了这个函数(在Jupyter)，它的灵感来自indraforyou的答案。它会自动绘制所有的层输出。您的图像必须具有(x, y, 1)形状，其中1代表1个通道。您只需调用plot_layer_outputs(…)来绘图。

%matplotlib inline
import matplotlib.pyplot as plt
from keras import backend as K

def get_layer_outputs():
    test_image = YOUR IMAGE GOES HERE!!!
    outputs    = [layer.output for layer in model.layers]          # all layer outputs
    comp_graph = [K.function([model.input]+ [K.learning_phase()], [output]) for output in outputs]  # evaluation functions

    # Testing
    layer_outputs_list = [op([test_image, 1.]) for op in comp_graph]
    layer_outputs = []

    for layer_output in layer_outputs_list:
        print(layer_output[0][0].shape, end='\n-------------------\n')
        layer_outputs.append(layer_output[0][0])

    return layer_outputs

def plot_layer_outputs(layer_number):    
    layer_outputs = get_layer_outputs()

    x_max = layer_outputs[layer_number].shape[0]
    y_max = layer_outputs[layer_number].shape[1]
    n     = layer_outputs[layer_number].shape[2]

    L = []
    for i in range(n):
        L.append(np.zeros((x_max, y_max)))

    for i in range(n):
        for x in range(x_max):
            for y in range(y_max):
                L[i][x][y] = layer_outputs[layer_number][x][y][i]


    for img in L:
        plt.figure()
        plt.imshow(img, interpolation='nearest')

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。

以下对我来说很简单:

model.layers[idx].output

上面是一个张量对象，所以你可以使用应用于张量对象的操作来修改它。

例如，要获取形状model.layers[idx].output.get_shape()

Idx是该层的索引，你可以从model.summary()中找到它

从https://keras.io/getting-started/faq/ how-can-i-obtain-the-output-of-an-intermediate-layer

一个简单的方法是创建一个新的模型，输出你感兴趣的图层:

from keras.models import Model

model = ...  # include here your original model

layer_name = 'my_layer'
intermediate_layer_model = Model(inputs=model.input,
                                 outputs=model.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model.predict(data)

或者，你可以构建一个Keras函数，它将返回给定特定输入的特定层的输出，例如:

from keras import backend as K

# with a Sequential model
get_3rd_layer_output = K.function([model.layers[0].input],
                                  [model.layers[3].output])
layer_output = get_3rd_layer_output([x])[0]

来自:https://github.com/philipperemy/keras-visualize-activations/blob/master/read_activations.py

import keras.backend as K

def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
    print('----- activations -----')
    activations = []
    inp = model.input

    model_multi_inputs_cond = True
    if not isinstance(inp, list):
        # only one input! let's wrap it in a list.
        inp = [inp]
        model_multi_inputs_cond = False

    outputs = [layer.output for layer in model.layers if
               layer.name == layer_name or layer_name is None]  # all layer outputs

    funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions

    if model_multi_inputs_cond:
        list_inputs = []
        list_inputs.extend(model_inputs)
        list_inputs.append(0.)
    else:
        list_inputs = [model_inputs, 0.]

    # Learning phase. 0 = Test mode (no dropout or batch normalization)
    # layer_outputs = [func([model_inputs, 0.])[0] for func in funcs]
    layer_outputs = [func(list_inputs)[0] for func in funcs]
    for layer_activations in layer_outputs:
        activations.append(layer_activations)
        if print_shape_only:
            print(layer_activations.shape)
        else:
            print(layer_activations)
    return activations

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。

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

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!

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]

这个答案基于:https://stackoverflow.com/a/59557567/2585501

打印单个图层的输出:

from tensorflow.keras import backend as K
layerIndex = 1
func = K.function([model.get_layer(index=0).input], model.get_layer(index=layerIndex).output)
layerOutput = func([input_data])  # input_data is a numpy array
print(layerOutput)

打印每一层的输出:

from tensorflow.keras import backend as K
for layerIndex, layer in enumerate(model.layers):
    func = K.function([model.get_layer(index=0).input], layer.output)
    layerOutput = func([input_data])  # input_data is a numpy array
    print(layerOutput)

以前的解决方案对我不起作用。我对这个问题的处理如下所示。

layer_outputs = []
for i in range(1, len(model.layers)):
    tmp_model = Model(model.layers[0].input, model.layers[i].output)
    tmp_output = tmp_model.predict(img)[0]
    layer_outputs.append(tmp_output)

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: