def examples(self, data_generator, num_examples=5):
"""
Prints some examples during training
Args:
data_generator:
"""
assert self.symbols
x, y = data_generator.next_batch(validation=True)
# input_strings = decode_output_sequences(x, symbols=SYMBOLS)
target_strings = decode_output_sequences(y, symbols=self.symbols)
model_output = self.predict(x)
pred_strings = decode_output_sequences(model_output, symbols=self.symbols)
print(target_strings[:num_examples])
print(pred_strings[:num_examples])
logging = tf.logging
flags.DEFINE_string(
"model", "small",
"A type of model. Possible options are: small, medium, large, zoneout.")
flags.DEFINE_string("data_path", None, "data_path")
FLAGS = flags.FLAGS
from program_generator import ProgramGenerator, SYMBOLS, SYMBOL_TO_IDX, INPUT_SEQ_LEN, OUTPUT_SEQ_LEN
program_generator = ProgramGenerator(batch_size=10,
program_length=1,
num_len=2)
x, y = program_generator.next_batch()
input_strings = decode_output_sequences(x, symbols=SYMBOLS)
target_strings = decode_output_sequences(y, symbols=SYMBOLS)
print(" Inputs:", input_strings)
print("Targets:", target_strings)
session.close()
tf.reset_default_graph()
session = tf.InteractiveSession()
hidden_units = 320
num_layers = 2
training_batch_size = 128
num_symbols = len(SYMBOL_TO_IDX)
program_model = Seq2SeqProgramModel(session=session,
hidden_units=hidden_units,
num_layers = 2
training_batch_size = 32
z_prob_cells = 0.05
z_prob_states = 0
num_symbols = len(SYMBOL_TO_IDX)
DEFAULT_LEARNING_RATE = 0.01
print('zoneout cells'+str(z_prob_cells)+'states'+str(z_prob_states))
#################
# DATA
#################
addition_generator = AdditionGenerator(batch_size=3)
x, y = addition_generator.next_batch()
input_strings = decode_output_sequences(x, symbols=SYMBOLS)
target_strings = decode_output_sequences(y, symbols=SYMBOLS)
print(" Inputs:", input_strings)
print("Targets:", target_strings)
session.close()
tf.reset_default_graph()
session = tf.InteractiveSession()
#################
# MODEL
#################
# Wrapper for the TF RNN cell
# For an LSTM, the 'cell' is a tuple containing state and cell
# We use TF's dropout to implement zoneout
