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]

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

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]

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

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。