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Removing dimension using reshape in keras?

Is it possible to remove a dimension using Reshape or any other function.

I have the following network.

import keras
from keras.layers.merge import Concatenate
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv2D, MaxPooling2D, Reshape, ZeroPadding2D
import numpy as np


#Number_of_splits = ((input_width-win_dim)+1)/stride_dim
splits = ((40-5)+1)/1
print splits


train_data_1 = np.random.randint(100,size=(100,splits,45,5,3))
test_data_1 = np.random.randint(100,size=(10,splits,45,5,3))
labels_train_data =np.random.randint(145,size=(100,15))
labels_test_data =np.random.randint(145,size=(10,15))


list_of_input = [Input(shape = (45,5,3)) for i in range(splits)]
list_of_conv_output = []
list_of_max_out = []
for i in range(splits):
    list_of_conv_output.append(Conv2D(filters = 145 , kernel_size = (15,3))(list_of_input[i])) #output dim: 36x(31,3,145)
    list_of_max_out.append((MaxPooling2D(pool_size=(2,2))(list_of_conv_output[i]))) #output dim: 36x(15,1,145)


merge = keras.layers.concatenate(list_of_max_out) #Output dim: (15,1,5220)
#reshape = Reshape((merge.shape[0],merge.shape[3]))(merge) # expected output dim: (15,145)


dense1 = Dense(units = 1000, activation = 'relu',    name = "dense_1")(merge)
dense2 = Dense(units = 1000, activation = 'relu',    name = "dense_2")(dense1)
dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2)






model = Model(inputs = list_of_input , outputs = dense3)
model.compile(loss="sparse_categorical_crossentropy", optimizer="adam")


print model.summary()


raw_input("SDasd")
hist_current = model.fit(x = [train_input[i] for i in range(100)],
                    y = labels_train_data,
                    shuffle=False,
                    validation_data=([test_input[i] for i in range(10)], labels_test_data),
                    validation_split=0.1,
                    epochs=150000,
                    batch_size = 15,
                    verbose=1)

The maxpooling layer creates an output with dimension (15,1,36) which i would like to remove the middle axis, so the output dimension end up being (15,36)..

If possible would I like to avoid specifying the outer dimension, or as i've tried use the prior layer dimension to reshape it.

#reshape = Reshape((merge.shape[0],merge.shape[3]))(merge) # expected output dim: (15,145)

I need my output dimension for the entire network to be (15,145), in which the middle dimension is causing some problems.

How do i remove the middle dimension?

like image 439
J.Down Avatar asked Apr 11 '17 03:04

J.Down


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How does reshape work in Keras?

Reshape class Layer that reshapes inputs into the given shape. Arbitrary, although all dimensions in the input shape must be known/fixed. Use the keyword argument input_shape (tuple of integers, does not include the samples/batch size axis) when using this layer as the first layer in a model.

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1 Answers

I wanted to remove all dimensions that are equal to 1, but not specify a specific size with Reshape so that my code does not break if I change the input size or number of kernels in a convolution. This works with the functional keras API on a tensorflow backend.

from keras.layers.core import Reshape

old_layer = Conv2D(#actualArguments) (older_layer)
#old_layer yields, e.g., a (None, 15,1,36) size tensor, where None is the batch size

newdim = tuple([x for x in old_layer.shape.as_list() if x != 1 and x is not None])
#newdim is now (15, 36). Reshape does not take batch size as an input dimension.
reshape_layer = Reshape(newdim) (old_layer)
like image 122
jeremysprofile Avatar answered Oct 06 '22 01:10

jeremysprofile