lunny/keras
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Modified comment and fixed batch_size

Browse Source
This commit is contained in:
Moto H 2015-07-07 14:22:25 +09:00
parent 14b014883a
commit 7ff158de41
1 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
examples/mnist_irnn.py
View File

@ -12,7 +12,7 @@ from keras.optimizers import RMSprop
from keras.utils import np_utils
'''
This is a reproduction of the IRNN experiment
This is a reproduction of the IRNN experiment
with pixel-by-pixel sequential MNIST in
"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units "
by Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
@ -23,8 +23,8 @@ from keras.utils import np_utils
Optimizer is replaced with RMSprop which yields more stable and steady
improvement.
0.80 train/test accuracy and 0.55 train/test loss after 70 epochs
(it's still underfitting at that point, though).
Reaches 0.93 train/test accuracy after 900 epochs (which roughly corresponds
to 1687500 steps in the original paper.)
'''
batch_size = 32
@ -64,7 +64,7 @@ model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
@ -79,9 +79,9 @@ model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
print('LSTM test score:', scores[0])
print('LSTM test accuracy:', scores[1])
print('LSTM test accuracy:', scores[1])