def classifier_gru_train_in_fn(train_file, test_file, batch_size,
sequence_length, input_dimension,
shuffle_capacity, shuffle_threads,
shuffle_min_after_dequeue):
with tf.name_scope('Input_Queue') as scope:
train_data_queue = tf.train.string_input_producer([train_file])
train_features, train_labels = gen_data.read_and_decode(
train_data_queue, batch_size, sequence_length, input_dimension,
shuffle_capacity, shuffle_threads, shuffle_min_after_dequeue)
test_data_queue = tf.train.string_input_producer([test_file])
test_features, test_labels = gen_data.read_and_decode(
test_data_queue, batch_size, sequence_length, input_dimension,
shuffle_capacity, shuffle_threads, shuffle_min_after_dequeue)
data_dict = {
'train_features': train_features,
'test_features': test_features
}
labels_dict = {
'train_labels': train_labels,
'test_labels': test_labels
}
return data_dict, labels_dict
def classifier_gru_train_in_fn(train_file, test_file, batch_size,
sequence_length, input_dimension,
shuffle_capacity, shuffle_threads,
shuffle_min_after_dequeue):
with tf.name_scope('Input_Queue') as scope:
train_data_queue = tf.train.string_input_producer([train_file])
train_features_dict, train_labels = gen_data.read_and_decode(
train_data_queue, batch_size, sequence_length, input_dimension,
shuffle_capacity, shuffle_threads, shuffle_min_after_dequeue)
test_data_queue = tf.train.string_input_producer([test_file])
test_features_dict, test_labels = gen_data.read_and_decode(
test_data_queue, batch_size, sequence_length, input_dimension,
shuffle_capacity, shuffle_threads, shuffle_min_after_dequeue)
train_features = train_features_dict['sequence_values']
test_features = test_features_dict['sequence_values']
# Mentioned modification is here:
# Get elements 1 to seqlen-1 for features
train_features_s = tf.slice(train_features, [0, 1, 0], [
train_features.shape[0], train_features.shape[1] - 1,
train_features.shape[2]
])
test_features_s = tf.slice(test_features, [0, 1, 0], [
test_features.shape[0], test_features.shape[1] - 1,
test_features.shape[2]
])
# And elements 0 to seqlen -2 for labels
train_labels = tf.slice(train_features, [0, 0, 0], [
train_features.shape[0], train_features.shape[1] - 1,
train_features.shape[2]
])
test_labels = tf.slice(test_features, [0, 0, 0], [
test_features.shape[0], test_features.shape[1] - 1,
test_features.shape[2]
])
data_dict = {
'train_features': {
'sequence_values': train_features_s
},
'test_features': {
'sequence_values': test_features_s
}
}
labels_dict = {
'train_labels': train_labels,
'test_labels': test_labels
}
return data_dict, labels_dict
def main():
# Create checkpoint+checkpoint_path
if not os.path.exists(config.checkpoint_path):
os.makedirs(config.checkpoint_path)
# Create training data.
if GENERATE_DATA or not os.path.exists(config.data_tmp_folder):
if not os.path.exists(config.data_tmp_folder):
os.makedirs(config.data_tmp_folder)
print("Generating Data CSV")
# List of lambdas: [lambda x: math.sin(x)]
gen_data.gen_function_vals_csv(-50, -50 + (config.epoch_size + config.test_epoch_size + config.sequence_length)*0.02, 0.02, lambda x: math.sin(x),
config.data_tmp_folder + 'sine.csv')
gen_data.gen_function_vals_csv(-50, -50 + (config.epoch_size + config.test_epoch_size + config.sequence_length)*0.02, 0.02, lambda x: x*0.8 + 0.04,
config.data_tmp_folder + 'lin.csv')
print("Reading Data from CSV")
sine_x, data_sine = gen_data.read_function_vals_csv('x', 'y', config.data_tmp_folder + 'sine.csv')
# sine_x: [TOTAL_LENGTH, 1]
# data_sine: [TOTAL_LENGTH, INPUT_DIMENSION]
lin_x, data_lin = gen_data.read_function_vals_csv('x', 'y', config.data_tmp_folder + 'lin.csv')
# lin_x: [TOTAL_LENGTH, 1]
# data_lin: [TOTAL_LENGTH, INPUT_DIMENSION]
print("Writing TFRecords")
datasequences = np.stack((data_sine, data_lin), axis=0)
# datasequences: [ OUTPUT_DIMENSION, TOTAL_LENGTH, INPUT_DIMENSION ]
functionsequences, labels = gen_data.all_sequences_from_datasequence(datasequences, config.sequence_length)
# functionsequences: [ TOTAL_SEQUENCE_NUM, SEQUENCE_LENGTH, INPUT_DIMENSION ]
# labels: [ TOTAL_SEQUENCE_NUM ]
# Set apart some test data
test_functionsequences, test_labels = gen_data.rand_sequences_from_datasequences(functionsequences, labels, config.test_epoch_size, True)
# test_functionsequences: [ TEST_EPOCH_SIZE, SEQUENCE_LENGTH, INPUT_DIMENSION ]
# test_labels: [ TEST_EPOCH_SIZE ]
# functionsequences: [ SEQUENCE_NUM, SEQUENCE_LENGTH, INPUT_DIMENSION ]
# labels: [ SEQUENCE_NUM ]
gen_data.function_sequences_to_tfrecord(functionsequences, labels, config.data_tmp_folder+config.data_tfrecord_filename)
gen_data.function_sequences_to_tfrecord(test_functionsequences, test_labels, config.data_tmp_folder+config.test_tfrecord_filename)
print('Setup Input Queue')
with tf.name_scope('Input_Queue') as scope:
data_queue = tf.train.string_input_producer([config.data_tmp_folder + config.data_tfrecord_filename])
test_queue = tf.train.string_input_producer([config.data_tmp_folder + config.test_tfrecord_filename])
sequences_batch, labels_batch = gen_data.read_and_decode(data_queue, config.batch_size, config.sequence_length, config.input_dimension, config.shuffle_capacity, config.shuffle_threads, config.shuffle_min_after_dequeue)
test_sequences_batch, test_labels_batch = gen_data.read_and_decode(test_queue, config.batch_size, config.sequence_length, config.input_dimension, config.shuffle_capacity, config.shuffle_threads, config.shuffle_min_after_dequeue)
# Global Step Counter
with tf.name_scope('Global_Step') as scope:
global_step = tf.Variable(0, trainable=False, name='Global_Step_Var')
increment_global_step_op = tf.assign(global_step, global_step + 1)
# Create model
print("Creating Model")
# Model
Hin = np.zeros([config.batch_size, config.hidden_layer_size *
config.hidden_layer_depth], dtype=np.float32)
# Hin: [ BATCH_SIZE, INTERNALSIZE * NLAYERS ]
train_H, train_keep, train_step, train_summary_op = lstmnet(
sequences_batch, labels_batch, global_step, "train", False)
test_H, test_keep, test_step, test_summary_op = lstmnet(
test_sequences_batch, test_labels_batch, global_step, "test", True)
# Setup logging with Tensorboard
print("Setup Tensorboard")
timestamp = str(math.trunc(time.time()))
graph_location = "log" + tempfile.mkdtemp()
print(colored(' Saving graph to: ' + graph_location, 'red'))
writer = tf.summary.FileWriter(graph_location, graph=tf.get_default_graph())
saver = tf.train.Saver()
# Limit used gpu memory.
print("Configuring Tensorflow")
tfconfig = tf.ConfigProto()
# tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.75
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# train model.
with tf.Session(config=tfconfig) as sess:
print("Setup")
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("Training")
for step in range(config.iters):
if step % config.summary_iters == 0: # summary step
_, training_summary, test_summary = sess.run([train_step, train_summary_op, test_summary_op],
feed_dict={train_keep: config.pkeep, train_H: Hin,
test_keep: 1.0, test_H: Hin})
saver.save(sess, config.checkpoint_path)
writer.add_summary(training_summary, step)
writer.add_summary(test_summary, step)
else:
_ = sess.run([train_step], feed_dict={train_keep: config.pkeep, train_H: Hin})
# Increment global step Counter
# sess.run(increment_global_step_op)
coord.request_stop()
coord.join(threads)