Y_test = np_utils.to_categorical(y_test, nb_classes)
model = NN_Model(n_epochs=nb_epoch, n_batch=batch_size, val_Freq=1)
model.add(Convolution2D(nb_filters, 1, nb_conv, nb_conv, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(nb_pool, nb_pool)))
model.add(Drop_out(0.25))
model.add(Flatten())
# the resulting image after conv and pooling is the original shape
# divided by the pooling with a number of filters for each "pixel"
# (the number of filters is determined by the last Conv2D)
model.add(FC_layer(nb_filters * (shapex / nb_pool) * (shapey / nb_pool), 128))
model.add(Activation('relu'))
model.add(Drop_out(0.5))
model.add(FC_layer(128, nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='Adadelta',
mask=False)
model.train(X_train, None, Y_train, X_test, None, Y_test)
#score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
##print('Test score:', score[0])
#print('Test accuracy:', score[1])
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = NN_Model(n_epochs=nb_epoch,n_batch=batch_size,val_Freq=1)
model.add(Convolution2D(nb_filters, 1, nb_conv, nb_conv, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(nb_pool, nb_pool)))
model.add(Drop_out(0.25))
model.add(Flatten())
# the resulting image after conv and pooling is the original shape
# divided by the pooling with a number of filters for each "pixel"
# (the number of filters is determined by the last Conv2D)
model.add(FC_layer(nb_filters * (shapex / nb_pool) * (shapey / nb_pool), 128))
model.add(Activation('relu'))
model.add(Drop_out(0.5))
model.add(FC_layer(128, nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='Adadelta',mask=False)
model.train(X_train, None , Y_train, X_test, None, Y_test)
#score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
##print('Test score:', score[0])
#print('Test accuracy:', score[1])
model.add(FC_layer(n_h,n_y))
model.add(Activation('softmax'))
model.compile(optimizer=optimizer,loss=loss)
filepath='save/review3.pkl'
if mode=='tr':
if os.path.isfile(filepath): model.load(filepath)
print '<training data>'
seq,seq_mask,targets=prepare_full_data_keras(train[0],train[1],n_maxlen)
print '<validation data>'
val,val_mask,val_targets=prepare_full_data_keras(valid[0],valid[1],n_maxlen)
model.train(seq,seq_mask,targets,val,val_mask,val_targets,verbose)
model.save(filepath)
##draw error graph
plt.close('all')
fig = plt.figure()
ax3 = plt.subplot(111)
plt.plot(model.errors)
plt.grid()
ax3.set_title('Training error')
plt.savefig('error.png')
elif mode=='te':
if os.path.isfile(filepath): model.load(filepath)
else:
