def test_create_forward_rnn(self):
tf.reset_default_graph()
with tf.Session():
model = LanguageModel(self.num_layers, self.hidden_size,
self.batch_size, self.max_input_seq_length,
self.max_target_seq_length, self.input_dim)
model.create_forward_rnn()
def generate_text(hyper_params):
with tf.Session() as sess:
# Create model
model = LanguageModel(hyper_params["num_layers"],
hyper_params["hidden_size"], 1, 1,
hyper_params["max_target_seq_length"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/language/")
# Start with a letter
text = "O"
for _ in range(10):
print(text, end="")
# Convert to an one-hot encoded vector
input_vec = dataprocessor.DataProcessor.get_str_to_one_hot_encoded(
hyper_params["char_map"], text, add_eos=False)
feat_vec = np.array(input_vec)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
prediction = model.process_input(sess, feat_vec, [1])
text = dataprocessor.DataProcessor.get_labels_str(
hyper_params["char_map"], prediction[0])
print(text)
return
def generate_text(hyper_params):
with tf.Session() as sess:
# Create model
model = LanguageModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1, 1,
hyper_params["max_target_seq_length"], hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/language/")
# Start with a letter
text = "O"
for _ in range(10):
print(text, end="")
# Convert to an one-hot encoded vector
input_vec = dataprocessor.DataProcessor.get_str_to_one_hot_encoded(hyper_params["char_map"], text,
add_eos=False)
feat_vec = np.array(input_vec)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
prediction = model.process_input(sess, feat_vec, [1])
text = dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction[0])
print(text)
return
def test_create_forward_rnn(self):
tf.reset_default_graph()
with tf.Session():
model = LanguageModel(self.num_layers, self.hidden_size, self.batch_size, self.max_input_seq_length,
self.max_target_seq_length, self.input_dim)
model.create_forward_rnn()