def validate_sentence(session, model, validation_batch, encoder_state, current_step):
encoder_inputs, single_decoder_inputs, decoder_weights = validation_batch.next()
print(BatchGenerator.batches2string(encoder_inputs))
print(BatchGenerator.batches2string(single_decoder_inputs))
# replicate to full batch size so we have multiple results agains the whole state
encoder_inputs = [np.repeat(x, BATCH_SIZE, axis=0) for x in encoder_inputs]
decoder_inputs = [np.repeat(x, BATCH_SIZE, axis=0) for x in single_decoder_inputs]
decoder_weights = [np.repeat(x, BATCH_SIZE, axis=0) for x in decoder_weights]
# _, eval_loss, prediction = model.step(sess, current_step - 1, encoder_inputs, decoder_inputs,
# decoder_weights, enc_state[-1:], 1.0, True)
_, eval_loss, prediction = model.step(session, current_step - 1, encoder_inputs, decoder_inputs,
decoder_weights, encoder_state, 1.0, True)
# split into 'no of batches' list then average across batches
reshaped = np.reshape(prediction, (prediction.shape[0] / BATCH_SIZE, BATCH_SIZE, prediction.shape[1]))
averaged = np.mean(reshaped, axis=1)
# now roll as in case of single batch
rolled = np.rollaxis(np.asarray([averaged]), 1, 0)
splitted = np.vsplit(rolled, rolled.shape[0])
squeezed = [np.squeeze(e,0) for e in splitted]
print(BatchGenerator.batches2string(squeezed))
# compute character to character perplexity
val_perp = float(np.exp(BatchGenerator.logprob(np.concatenate(squeezed),
np.concatenate(single_decoder_inputs[1:]))))
print('--validation perp.: %.2f' % val_perp)
return val_perp
def run_test():
# test batch generation
print('download and read data')
filename = maybe_download('text8.zip', 31344016)
# Read data
text = read_data(filename)
# create datasets
valid_size = 1000
valid_text = text[:valid_size]
train_text = text[valid_size:]
# train_size = len(train_text)
# create batch generators
train_batches = BatchGenerator(train_text, BATCH_SIZE, MIN_CHARS_IN_BATCH, MAX_CHARS_IN_BATCH, reverse_encoder_input=True)
valid_batches = BatchGenerator(valid_text, 1, MIN_CHARS_IN_BATCH, MAX_CHARS_IN_BATCH)
# print(BatchGenerator.characters(train_batches.next()[0]))
print('test main batch generator')
e_bs, d_bs, dw_bs = train_batches.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
e_bs, d_bs, dw_bs = train_batches.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
e_bs, d_bs, dw_bs = valid_batches.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
e_bs, d_bs, dw_bs = valid_batches.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
print('test random english generator')
random_batch = RandomWordsBatchGenerator(2, MIN_CHARS_IN_BATCH, MAX_CHARS_IN_BATCH,
reverse_encoder_input=False)
for _ in range(10):
e_bs, d_bs, dw_bs = random_batch.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
print('test random string gen with padding')
random_str_batch = ReverseStringBatchGenerator(1, 8, 8,
reverse_encoder_input=False)
e_bs, d_bs, dw_bs = random_str_batch.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)
random_str_batch = ReverseStringBatchGenerator(2, 8, 16,
reverse_encoder_input=False)
e_bs, d_bs, dw_bs = random_str_batch.next()
print(BatchGenerator.batches2string(e_bs))
print(BatchGenerator.batches2string(d_bs))
BatchGenerator.verify_weights(d_bs, dw_bs)