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Bug in Keras batch_norm
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# Script for reproducing a BatchNormalization bug | |
# https://github.com/fchollet/keras/issues/5643 | |
from keras.models import Sequential, Model | |
from keras.layers import Dense, BatchNormalization | |
import numpy as np | |
m1 = Sequential([ | |
Dense(output_dim=5, input_dim=5), | |
BatchNormalization(), | |
Dense(output_dim=5), | |
]) | |
m2 = Sequential([ | |
Dense(output_dim=5, input_dim=5), | |
BatchNormalization(), # Without this line, this script runs to completion | |
Dense(output_dim=5), | |
]) | |
x = np.ones((3, 5)) | |
y = np.ones((3, 5)) | |
m1.compile(optimizer='sgd', loss='categorical_crossentropy') | |
m2.compile(optimizer='sgd', loss='categorical_crossentropy') | |
h = m2.fit(x, y, verbose=0) # Fitting m2 before creating m3 removes the bug | |
m3 = Model(input=m1.inputs, output=m2(m1(m1.inputs))) | |
m3.compile(optimizer='sgd', loss='categorical_crossentropy') | |
h1 = m1.fit(x, y, verbose=0) | |
h2 = m2.fit(x, y, verbose=0) # This line no longer fails if m2 if fitted before | |
h3 = m3.fit(x, y, verbose=0) |
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