def batch_train(train, val, model_path):
trainX, trainY = train
valX, valY = val
nn = Classifier(
layers=[
Convolution('Rectifier',
channels=100,
kernel_shape=(5, WORD_DIM),
border_mode='valid'
#pool_shape=(3,1),
#pool_type='max'
),
Layer('Rectifier', units=900, dropout=0.5),
Layer('Softmax')
],
batch_size=50,
learning_rate=0.02,
normalize='dropout',
verbose=True)
nn.n_iter = 100
print 'Net created...'
try:
nn.fit(trainX, trainY)
except KeyboardInterrupt:
pickle.dump(nn, open(model_path, 'wb'))
pickle.dump(nn, open(model_path, 'wb'))
print 'Done, final model saved'
print 'Testing'
#Accuracy on the validation set
print 'Validation accuracy:', batch_test(model_path, val)
def batch_train(train,val,model_path):
trainX,trainY = train
valX,valY = val
nn = Classifier(layers = [
Convolution('Rectifier',
channels=100,
kernel_shape=(5,WORD_DIM),
border_mode='valid'
#pool_shape=(3,1),
#pool_type='max'
),
Layer('Rectifier',units=900,dropout=0.5),
Layer('Softmax')],
batch_size = 50,
learning_rate = 0.02,
normalize='dropout',
verbose = True)
nn.n_iter = 100
print 'Net created...'
try:
nn.fit(trainX,trainY)
except KeyboardInterrupt:
pickle.dump(nn,open(model_path,'wb'))
pickle.dump(nn,open(model_path,'wb'))
print 'Done, final model saved'
print 'Testing'
#Accuracy on the validation set
print 'Validation accuracy:',batch_test(model_path,val)
def train(X, Y):
print X.shape
print Y.shape
trainX = X[:int(X.shape[0] * 0.7), :, :]
trainY = Y[:int(Y.shape[0] * 0.7), :]
valX = X[int(X.shape[0] * 0.7):int(X.shape[0] * 0.8), :, :]
valY = Y[int(Y.shape[0] * 0.7):int(Y.shape[0] * 0.8), :]
testX = X[int(X.shape[0] * 0.8):, :, :]
testY = Y[int(Y.shape[0] * 0.8):, :]
print 'Train, Val, Test'
print trainX.shape, ',', trainY.shape, '--', valX.shape, ',', valY.shape, '--', testX.shape, ',', testY.shape
nn = Classifier(
layers=[
Convolution('Rectifier', channels=1, kernel_shape=(5, WORD_DIM)),
Layer('Rectifier', units=300),
Layer('Rectifier', units=300),
Layer('Softmax')
],
#valid_set = (valX,valY),
learning_rate=0.02, #0.05, #0.001,
#normalize='batch',
verbose=True)
print 'Net created...'
#Load net here --always CHECK HERE before starting -- DO THIS NOW, WE WANT TO CONTINUE FROM HERE ON
nn = pickle.load(open(model_path, 'rb'))
for i in range(100):
try:
nn.n_iter = 5
nn.fit(trainX, trainY)
pickle.dump(nn, open(model_path, 'wb'))
nn = pickle.load(open(model_path, 'rb'))
except KeyboardInterrupt:
pickle.dump(nn, open(model_path, 'wb'))
print 'Saved model after keyboard interrupt'
pickle.dump(nn, open(model_path, 'wb'))
print 'Temp model saved'
#try:
# nn.fit(trainX,trainY)
#except KeyboardInterrupt:
# pickle.dump(nn,open(model_path,'wb'))
print 'Done, final model saved'
def train(X,Y):
print X.shape
print Y.shape
trainX = X[:int(X.shape[0]*0.7),:,:]
trainY = Y[:int(Y.shape[0]*0.7),:]
valX = X[int(X.shape[0]*0.7):int(X.shape[0]*0.8),:,:]
valY = Y[int(Y.shape[0]*0.7):int(Y.shape[0]*0.8),:]
testX = X[int(X.shape[0]*0.8):,:,:]
testY = Y[int(Y.shape[0]*0.8):,:]
print 'Train, Val, Test'
print trainX.shape,',',trainY.shape,'--',valX.shape,',',valY.shape,'--',testX.shape,',',testY.shape
nn = Classifier(
layers = [
Convolution('Rectifier',channels=1,kernel_shape=(5,WORD_DIM)),
Layer('Rectifier',units=300),
Layer('Rectifier',units=300),
Layer('Softmax')],
#valid_set = (valX,valY),
learning_rate = 0.02, #0.05, #0.001,
#normalize='batch',
verbose = True)
print 'Net created...'
#Load net here --always CHECK HERE before starting -- DO THIS NOW, WE WANT TO CONTINUE FROM HERE ON
nn = pickle.load(open(model_path,'rb'))
for i in range(100):
try:
nn.n_iter = 5
nn.fit(trainX,trainY)
pickle.dump(nn,open(model_path,'wb'))
nn = pickle.load(open(model_path,'rb'))
except KeyboardInterrupt:
pickle.dump(nn,open(model_path,'wb'))
print 'Saved model after keyboard interrupt'
pickle.dump(nn,open(model_path,'wb'))
print 'Temp model saved'
#try:
# nn.fit(trainX,trainY)
#except KeyboardInterrupt:
# pickle.dump(nn,open(model_path,'wb'))
print 'Done, final model saved'
