def train_numpy():
model = RNNNumpy(Config._VOCABULARY_SIZE, hidden_dim=Config._HIDDEN_DIM)
t1 = time.time()
model.sgd_step(X_train[10], y_train[10], Config._LEARNING_RATE)
t2 = time.time()
print "SGD Step time: %f milliseconds" % ((t2 - t1) * 1000.)
model.train_with_sgd(X_train, y_train, nepoch=Config._NEPOCH, learning_rate=Config._LEARNING_RATE)
# train_with_sgd(model, X_train, y_train, nepoch=_NEPOCH, learning_rate=_LEARNING_RATE)
if Config._MODEL_FILE != None:
print "start saving model..."
save_model_parameters_numpy(Config._MODEL_FILE, model)
print "model saved!"
import preprocess
from rnn_numpy import RNNNumpy
from rnn_theano import RNNTheano
import numpy as np
import cProfile
X_train,y_train,vocabulary_size = preprocess.create_train_data()
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
#cProfile.run("model.numpy_sdg_step(X_train[10], y_train[10], 0.005)")
#print("----------------------------------------------------------------")
np.random.seed(10)
model_theano = RNNTheano(vocabulary_size)
#cProfile.run("model_theano.train_with_sgd(X_train[10], y_train[10], 0.005)")
print("----------------------------------------------------------------")
losses_numpy = model.train_with_sgd(X_train[:100], y_train[:100], nepoch=5, evaluate_loss_after=1)
losses_theano = model_theano.train_with_sgd(X_train[:100], y_train[:100], nepoch=5, evaluate_loss_after=1)
import preprocess
from rnn_numpy import RNNNumpy
from rnn_theano import RNNTheano
import numpy as np
import cProfile
X_train, y_train, vocabulary_size = preprocess.create_train_data()
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
#cProfile.run("model.numpy_sdg_step(X_train[10], y_train[10], 0.005)")
#print("----------------------------------------------------------------")
np.random.seed(10)
model_theano = RNNTheano(vocabulary_size)
#cProfile.run("model_theano.train_with_sgd(X_train[10], y_train[10], 0.005)")
print("----------------------------------------------------------------")
losses_numpy = model.train_with_sgd(X_train[:100],
y_train[:100],
nepoch=5,
evaluate_loss_after=1)
losses_theano = model_theano.train_with_sgd(X_train[:100],
y_train[:100],
nepoch=5,
evaluate_loss_after=1)
