def predict_unseen_data():
"""Step 0: load trained model and parameters"""
params = json.loads(open('./parameters.json').read())
checkpoint_dir = 'C:\Users\sai smaran chinthala\Desktop\Spring-2018\Python\DL\CN/trained_model_1510877046'
if not checkpoint_dir.endswith('/'):
checkpoint_dir += '/'
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir + 'checkpoints')
logging.critical('Loaded the trained model: {}'.format(checkpoint_file))
"""Step 1: load data for prediction"""
test_file = '/Users/bhavyateja/Masters/PythonDeepLearning/DL_4/New folder/small_samples.json'
test_examples = json.loads(open(test_file).read())
# labels.json was saved during training, and it has to be loaded during prediction
labels = json.loads(open('./labels.json').read())
one_hot = np.zeros((len(labels), len(labels)), int)
np.fill_diagonal(one_hot, 1)
label_dict = dict(zip(labels, one_hot))
x_raw = [example['consumer_complaint_narrative'] for example in test_examples]
x_test = [dataHelpers.clean_str(x) for x in x_raw]
logging.info('The number of x_test: {}'.format(len(x_test)))
y_test = None
if 'product' in test_examples[0]:
y_raw = [example['product'] for example in test_examples]
y_test = [label_dict[y] for y in y_raw]
logging.info('The number of y_test: {}'.format(len(y_test)))
vocab_path = os.path.join(checkpoint_dir, "vocab.pickle")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
x_test = np.array(list(vocab_processor.transform(x_test)))
"""Step 2: compute the predictions"""
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
input_x = graph.get_operation_by_name("input_x").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
batches = dataHelpers.batch_iter(list(x_test), params['batch_size'], 1, shuffle=False)
all_predictions = []
for x_test_batch in batches:
batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
all_predictions = np.concatenate([all_predictions, batch_predictions])
if y_test is not None:
y_test = np.argmax(y_test, axis=1)
correct_predictions = sum(all_predictions == y_test)
logging.critical('The accuracy is: {}'.format(correct_predictions / float(len(y_test))))
logging.critical('The prediction is complete')
def train_cnn():
"""Step 0: load sentences, labels, and training parameters"""
#train_file = sys.argv[1]
input_file = 'C:/Users/bvkka/Desktop/New folder/consumer_complaints.csv.zip'
x_raw, y_raw, df, labels = dataHelpers.load_data_and_labels(input_file)
#parameter_file = sys.argv[0]
#paramater_file='C:/Users/bvkka/Desktop/New folder/parameters.json'
# Model Hyper parameters
tf.flags.DEFINE_integer("embedding_dim", 40, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer("num_filters", 32, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 1, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
#params = json.loads(open(parameter_file).read())
"""Step 1: pad each sentence to the same length and map each word to an id"""
max_document_length = max([len(x.split(' ')) for x in x_raw])
logging.info('The maximum length of all sentences: {}'.format(max_document_length))
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform(x_raw)))
y = np.array(y_raw)
"""Step 2: split the original dataset into train and test sets"""
x_, x_test, y_, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
"""Step 3: shuffle the train set and split the train set into train and dev sets"""
shuffle_indices = np.random.permutation(np.arange(len(y_)))
x_shuffled = x_[shuffle_indices]
y_shuffled = y_[shuffle_indices]
x_train, x_dev, y_train, y_dev = train_test_split(x_shuffled, y_shuffled, test_size=0.1)
"""Step 4: save the labels into labels.json since predict.py needs it"""
#with open('./labels.json', 'w') as outfile:
# json.dump(labels, outfile, indent=4)
logging.info('x_train: {}, x_dev: {}, x_test: {}'.format(len(x_train), len(x_dev), len(x_test)))
logging.info('y_train: {}, y_dev: {}, y_test: {}'.format(len(y_train), len(y_dev), len(y_test)))
"""Step 5: build a graph and cnn object"""
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "trained_model_" + timestamp))
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables())
# One training step: train the model with one batch
def train_step(x_batch, y_batch):
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob}
_, step, loss, acc = sess.run([train_op, global_step, cnn.loss, cnn.accuracy], feed_dict)
# One evaluation step: evaluate the model with one batch
def dev_step(x_batch, y_batch):
feed_dict = {cnn.input_x: x_batch, cnn.input_y: y_batch, cnn.dropout_keep_prob: 1.0}
step, loss, acc, num_correct = sess.run([global_step, cnn.loss, cnn.accuracy, cnn.num_correct], feed_dict)
return num_correct
# Save the word_to_id map since predict.py needs it
vocab_processor.save(os.path.join(out_dir, "vocab.pickle"))
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
# Training starts here
train_batches = dataHelpers.batch_iter(list(zip(x_train, y_train)),FLAGS.batch_size,FLAGS.num_epochs)
best_accuracy, best_at_step = 0, 0
"""Step 6: train the cnn model with x_train and y_train (batch by batch)"""
for train_batch in train_batches:
x_train_batch, y_train_batch = zip(*train_batch)
train_step(x_train_batch, y_train_batch)
current_step = tf.train.global_step(sess, global_step)
"""Step 6.1: evaluate the model with x_dev and y_dev (batch by batch)"""
if current_step % FLAGS.evaluate_every == 0:
dev_batches = dataHelpers.batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1)
total_dev_correct = 0
for dev_batch in dev_batches:
x_dev_batch, y_dev_batch = zip(*dev_batch)
num_dev_correct = dev_step(x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
dev_accuracy = float(total_dev_correct) / len(y_dev)
logging.critical('Accuracy on dev set: {}'.format(dev_accuracy))
"""Step 6.2: save the model if it is the best based on accuracy on dev set"""
if dev_accuracy >= best_accuracy:
best_accuracy, best_at_step = dev_accuracy, current_step
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
logging.critical('Saved model at {} at step {}'.format(path, best_at_step))
logging.critical('Best accuracy is {} at step {}'.format(best_accuracy, best_at_step))
"""Step 7: predict x_test (batch by batch)"""
test_batches = dataHelpers.batch_iter(list(zip(x_test, y_test)),FLAGS.batch_size, 1)
total_test_correct = 0
for test_batch in test_batches:
x_test_batch, y_test_batch = zip(*test_batch)
num_test_correct = dev_step(x_test_batch, y_test_batch)
total_test_correct += num_test_correct
test_accuracy = float(total_test_correct) / len(y_test)
logging.critical('Accuracy on test set is {} based on the best model {}'.format(test_accuracy, path))
logging.critical('The training is complete')
