I have trained a binary classification model with CNN, and here is my code
model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
border_mode='valid',
input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
# (16, 16, 32)
model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
# (8, 8, 64) = (2048)
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2)) # define a binary classification problem
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
verbose=1,
validation_data=(x_test, y_test))
And here, I wanna get the output of each layer just like TensorFlow, how can I do that?
This question is related to
python
tensorflow
deep-learning
keras
Previous solutions were not working for me. I handled this issue as shown below.
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)
Assuming you have:
1- Keras pre-trained model
.
2- Input x
as image or set of images. The resolution of image should be compatible with dimension of the input layer. For example 80*80*3 for 3-channels (RGB) image.
3- The name of the output layer
to get the activation. For example, "flatten_2" layer. This should be include in the layer_names
variable, represents name of layers of the given model
.
4- batch_size
is an optional argument.
Then you can easily use get_activation
function to get the activation of the output layer
for a given input x
and pre-trained model
:
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
From: 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
Wanted to add this as a comment (but don't have high enough rep.) to @indraforyou's answer to correct for the issue mentioned in @mathtick's comment. To avoid the InvalidArgumentError: input_X:Y is both fed and fetched.
exception, simply replace the line outputs = [layer.output for layer in model.layers]
with outputs = [layer.output for layer in model.layers][1:]
, i.e.
adapting indraforyou's minimal working example:
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
p.s. my attempts trying things such as outputs = [layer.output for layer in model.layers[1:]]
did not work.
In case you have one of the following cases:
InvalidArgumentError: input_X:Y is both fed and fetched
You need to do the following changes:
outputs
variablefunctors
loopMinimum example:
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]
Based on all the good answers of this thread, I wrote a library to fetch the output of each layer. It abstracts all the complexity and has been designed to be as user-friendly as possible:
https://github.com/philipperemy/keract
It handles almost all the edge cases
Hope it helps!
Following looks very simple to me:
model.layers[idx].output
Above is a tensor object, so you can modify it using operations that can be applied to a tensor object.
For example, to get the shape model.layers[idx].output.get_shape()
idx
is the index of the layer and you can find it from model.summary()
Well, other answers are very complete, but there is a very basic way to "see", not to "get" the shapes.
Just do a model.summary()
. It will print all layers and their output shapes. "None" values will indicate variable dimensions, and the first dimension will be the batch size.
This answer is based on: https://stackoverflow.com/a/59557567/2585501
To print the output of a single layer:
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)
To print output of every layer:
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)
I wrote this function for myself (in Jupyter) and it was inspired by indraforyou's answer. It will plot all the layer outputs automatically. Your images must have a (x, y, 1) shape where 1 stands for 1 channel. You just call plot_layer_outputs(...) to plot.
%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')
From https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer
One simple way is to create a new Model that will output the layers that you are interested in:
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)
Alternatively, you can build a Keras function that will return the output of a certain layer given a certain input, for example:
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]
Source: Stackoverflow.com