keras give your own name to a layer sequential model

Keras give your own name to a layer sequential model code#

# only one input! let's wrap it in a list. L = layer_outputsĭef get_activations(model, model_inputs, print_shape_only=False, layer_name=None):

Layer_outputs_list = ) for op in comp_graph] %matplotlib inlineĬomp_graph = +, ) for output in outputs] # evaluation functions You just call plot_layer_outputs(.) to plot. Your images must have a (x, y, 1) shape where 1 stands for 1 channel. It will plot all the layer outputs automatically. I wrote this function for myself (in Jupyter) and it was inspired by indraforyou's answer. Instead this is a much better way as you don't need multiple functions but a single function giving you the list of all outputs: from keras import backend as Kįunctor = K.function(, outputs ) # evaluation function I just realized that the previous answer is not that optimized as for each function evaluation the data will be transferred CPU->GPU memory and also the tensor calculations needs to be done for the lower layers over-n-over.

Keras give your own name to a layer sequential model code#

So if you remove the dropout layer in your code you can simply use: from keras import backend as K Now K.learning_phase() is required as an input as many Keras layers like Dropout/Batchnomalization depend on it to change behavior during training and test time. K.function creates theano/tensorflow tensor functions which is later used to get the output from the symbolic graph given the input. Note: To simulate Dropout use learning_phase as 1. Outputs = # all layer outputsįunctors =, ) for out in outputs] # evaluation functions You can easily get the outputs of any layer by using: įor all layers use this: from keras import backend as K pile(loss='categorical_crossentropy',Īnd here, I wanna get the output of each layer just like TensorFlow, how can I do that? Model.add(Dense(2)) # define a binary classification problem

Model.add(Convolution2D(nb_filters*2, kernel_size, kernel_size)) Model.add(MaxPooling2D(pool_size=pool_size)) Model.add(Convolution2D(nb_filters, kernel_size, kernel_size)) Model.add(Convolution2D(nb_filters, kernel_size, kernel_size, I have trained a binary classification model with CNN, and here is my code model = Sequential()