假设您有：

1- Keras预训练model 。

2-输入x作为图像或一组图像。图像的分辨率应与输入层的尺寸兼容。例如对于3通道（RGB）图像为80 * 80 * 3 。

3-要激活的输出layer的名称。例如，“ flatten_2”层。这应该包含在layer_names变量中，代表给定model的图层名称。

4- batch_size是一个可选参数。

然后，您可以轻松地使用get_activation函数来获取给定输入x和预训练model的输出layer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

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

一种简单的方法是创建一个新模型，该模型将输出您感兴趣的图层：

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)

另外，您可以构建Keras函数，该函数将在给定特定输入的情况下返回特定图层的输出，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]

以下对我来说看起来很简单：

model.layers[idx].output

上面是张量对象，因此您可以使用可应用于张量对象的操作对其进行修改。

例如，获得形状model.layers[idx].output.get_shape()

idx是图层的索引，您可以从model.summary()找到它

如果您具有以下情况之一：

• 错误： InvalidArgumentError: input_X:Y is both fed and fetched
• 多输入的情况

您需要进行以下更改：

• 在outputs变量中为输入层添加过滤器
• functors循环上的最小变化

最小示例：

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]

好吧，其他答案也很完整，但是有一种非常基本的方法可以“看到”形状，而不是“获得”形状。

只需执行model.summary() 。它将打印所有图层及其输出形状。 “无”值将指示可变尺寸，而第一维将是批量大小。

我为自己编写了此函数（在Jupyter中），它受到了indraforyou的回答的启发。它将自动绘制所有图层输出。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（...）即可进行绘制。

%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')

想要将其添加为@indraforyou的答案作为注释（但没有足够高的声望）以纠正@mathtick的注释中提到的问题。为了避免InvalidArgumentError: input_X:Y is both fed and fetched.例外，只需将outputs = [layer.output for layer in model.layers] outputs = [layer.output for layer in model.layers][1:] ，即

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

ps我尝试诸如outputs = [layer.output for layer in model.layers[1:]]类的尝试没有用。

基于该线程的所有良好答案，我编写了一个库来获取每一层的输出。它抽象了所有复杂性，并被设计为尽可能易于使用：

https://github.com/philipperemy/keract

它处理几乎所有边缘情况

希望能帮助到你！

您可以使用以下model.layers[index].output轻松获取任何图层的输出： model.layers[index].output

对于所有图层，请使用以下命令：

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，请在layer_outs learning_phase用作1.否则，请使用0.

编辑：（基于评论）

K.function创建theano / tensorflow张量函数，该函数随后用于从给定输入的符号图中获取输出。

现在需要K.learning_phase()作为输入，因为许多Keras层（如Dropout / Batchnomalization）都依赖它来在训练和测试期间更改行为。

因此，如果您删除代码中的辍学层，则可以简单地使用：

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

我只是意识到，先前的答案并不是针对每个函数评估进行了优化，因为数据将被传输到CPU-> GPU内存中，并且还需要对低层进行n-n-over的张量计算。

相反，这是一种更好的方法，因为您不需要多个函数，而只需一个函数即可为您提供所有输出的列表：

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

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