def sample_text(sess, data_provider, iteration):
model = RNNModel(data_provider.vocabulary_size, batch_size=1, sequence_length=1, hidden_layer_size=HIDDEN_LAYER_SIZE, cells_size=CELLS_SIZE, training=False)
text = model.sample(sess, data_provider.chars, data_provider.vocabulary, TEXT_SAMPLE_LENGTH)#.encode("utf-8")
output = open(output_file, "a")
output.write("Iteration: " + str(iteration) + "\n")
output.write(str(text) + "\n")
output.write("\n")
output.close()
def sample_text(sess, data_provider, iteration):
model = RNNModel(data_provider.vocabulary_size,
batch_size=1,
sequence_length=1,
hidden_layer_size=HIDDEN_LAYER_SIZE,
cells_size=CELLS_SIZE,
training=False)
text = model.sample(sess, data_provider.chars, data_provider.vocabulary,
TEXT_SAMPLE_LENGTH).encode("utf-8")
with open(output_file, "a") as output:
output.write("Iteration: " + str(iteration) + "\n")
output.write(text + "\n")
output.write("\n")
analysis = get_linguistic_analysis(text)
print(analysis)
with open(data_dir + "analysis.txt", mode="a",
encoding='utf-8') as analysis_file:
analysis_file.write("Iteration: " + str(iteration) + "\n")
analysis_file.write(analysis)
analysis_file.write("\n")
def sample_text(sess, data_provider, iteration):
model = RNNModel(data_provider.vocabulary_size, batch_size=1, sequence_length=1, hidden_layer_size=HIDDEN_LAYER_SIZE, cells_size=CELLS_SIZE, training=False)
text = model.sample(sess, data_provider.chars, data_provider.vocabulary, TEXT_SAMPLE_LENGTH).encode('utf-8')
with open(output_file, 'a') as output:
output.write(f'Iteration: {iteration}\n{text}\n\n')