# Word to predict.
pred_word = word2vec_vocab[words[i]]
target_vals.append(pred_word)
if i - 1 == MAX_SEQ_LEN:
break
encoded_sequences = np.vstack(encoded_sequences).astype('int32')
masks = np.vstack(masks).astype('float32')
y = np.vstack(target_vals).astype('int32')
output_layer, train_func, test_func, predict_func, get_hidden_func = word_prediction_network(BATCH_SIZE, word_embedding_size, num_words, MAX_SEQ_LEN, WEIGHTS, NUM_UNITS_GRU, learning_rate)
estimator = LasagneNet(output_layer, train_func, test_func, predict_func, get_hidden_func, on_epoch_finished=[SaveParams('save_params','word_embedding', save_interval = 1)])
# estimator.draw_network() # requires networkx package
X_train = {'X': encoded_sequences[:train_split], 'X_mask': masks[:train_split]}
y_train = y[:train_split]
X_test = {'X': encoded_sequences[train_split:test_split], 'X_mask': masks[train_split:test_split]}
y_test = y[train_split:test_split]
train = False
if train:
estimator.fit(X_train, y_train)
else:
estimator.load_weights_from('saved_params')
word2vec_vocab_rev = dict(zip(word2vec_vocab.values(), word2vec_vocab.keys())) # Maps indeces to words.
predictions = estimator.predict(X_test)
return train_func(X, y.reshape((-1,1)))
def test_function(X, y):
return test_func(X, y.reshape((-1,1)))
def predict_function(X):
return predict_func(X)
return l_out, train_function, test_function, predict_function, learning_rate
from sklearn import datasets
boston = datasets.load_boston()
num_epochs = 20
train_x, test_x, train_y, test_y = train_test_split(boston['data'], boston['target'], test_size=0.05)
l_output, train_function, test_function, predict_function, learning_rate = create_lasagne_network(train_x.shape[1])
estimator = LasagneNet(l_output, train_function, test_function, predict_function,is_regression=True,batch_iterator_train=BatchIterator(256),max_epochs=num_epochs,
on_epoch_finished=[AdjustVariable('learning_rate',start=0.03, stop=0.000001,end_epoch=num_epochs),
SaveParams('save_params','C:/params/rossman/', save_interval = 50)])
X = {'X' : train_x.astype('float32')}
estimator.fit(X, train_y.astype('float32'))
X = {'X' : test_x.astype('float32')}
pred = estimator.predict(X).reshape((-1,1))
print np.mean((pred-test_y)**2)
# TODO: use response also, so I can see the difference between predicted and actual.
encoded_sequences = np.vstack(encoded_sequences).astype("int32")
masks = np.vstack(masks).astype("float32")
X_test = {"X": encoded_sequences, "X_mask": masks}
output_layer, train_func, test_func, predict_func = word_prediction_network(
BATCH_SIZE, word_embedding_size, num_words, MAX_SEQ_LEN, WEIGHTS, NUM_UNITS_GRU, learning_rate
)
estimator = LasagneNet(
output_layer,
train_func,
test_func,
predict_func,
on_epoch_finished=[SaveParams("save_params", "word_embedding", save_interval=1)],
)
# estimator.draw_network() # requires networkx package
train = True
if train:
estimator.fit(X_train, y_train)
else:
estimator.load_weights_from("word_embedding/saved_params_3")
pred_sents = []
# For each test example, predict the response.
for idx in xrange(X_test["X"].shape[0]):
pred_words = []
