keras_lstm.py with fix of not working gfile approach

[lorenzznerol]

# # start in commandline: python keras_lstm.py [-h] [--data_path DATA PATH] runopt
# 'An integer: 1 to train, 2 to test'
# i.e.: python keras_lstm.py 1
# or: python keras_lstm.py 2

from __future__ import print_function
import collections
import os
import tensorflow as tf
from keras.models import Sequential, load_model
from keras.layers import Dense, Activation, Embedding, Dropout, TimeDistributed
from keras.layers import LSTM
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.callbacks import ModelCheckpoint
import numpy as np
import argparse
import pandas as pd

"""To run this code, you'll need to first download and extract the text dataset
    from here: http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz. Change the
    data_path variable below to your local exraction path"""

# data_path = "C:\\Users\Andy\Documents\simple-examples\data"

parser = argparse.ArgumentParser()
parser.add_argument('run_opt', type=int, default=1, help='An integer: 1 to train, 2 to test')
parser.add_argument('--data_path', type=str, default=data_path, help='The full path of the training data')
args = parser.parse_args()
if args.data_path:
    data_path = args.data_path

def read_words(filename):
    # Changed from tf.gfile to tf.io.gfile
    # https://github.com/tensorflow/tensorflow/issues/31315
    # with tf.io.gfile.GFile(filename, "r") as f:
    # https://github.com/tensorflow/tensorflow/issues/33563
    #     return f.read().decode("utf-8").replace("\n", "<eos>").split()
    data = pd.read_csv(filename, encoding='utf-8', header = None)
    data = ''.join([str(x) for x in data[0]]).split()
    return data

def build_vocab(filename):
    data = read_words(filename)

    counter = collections.Counter(data)
    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

    words, _ = list(zip(*count_pairs))
    word_to_id = dict(zip(words, range(len(words))))

    return word_to_id


def file_to_word_ids(filename, word_to_id):
    data = read_words(filename)
    return [word_to_id[word] for word in data if word in word_to_id]


def load_data():
    # get the data paths
    train_path = os.path.join(data_path, "ptb.train.txt")
    valid_path = os.path.join(data_path, "ptb.valid.txt")
    test_path = os.path.join(data_path, "ptb.test.txt")

    # build the complete vocabulary, then convert text data to list of integers
    word_to_id = build_vocab(train_path)
    train_data = file_to_word_ids(train_path, word_to_id)
    valid_data = file_to_word_ids(valid_path, word_to_id)
    test_data = file_to_word_ids(test_path, word_to_id)
    vocabulary = len(word_to_id)
    reversed_dictionary = dict(zip(word_to_id.values(), word_to_id.keys()))

    print(train_data[:5])
    print(word_to_id)
    print(vocabulary)
    print(" ".join([reversed_dictionary[x] for x in train_data[:10]]))
    return train_data, valid_data, test_data, vocabulary, reversed_dictionary

train_data, valid_data, test_data, vocabulary, reversed_dictionary = load_data()


class KerasBatchGenerator(object):

    def __init__(self, data, num_steps, batch_size, vocabulary, skip_step=5):
        self.data = data
        self.num_steps = num_steps
        self.batch_size = batch_size
        self.vocabulary = vocabulary
        # this will track the progress of the batches sequentially through the
        # data set - once the data reaches the end of the data set it will reset
        # back to zero
        self.current_idx = 0
        # skip_step is the number of words which will be skipped before the next
        # batch is skimmed from the data set
        self.skip_step = skip_step

    def generate(self):
        x = np.zeros((self.batch_size, self.num_steps))
        y = np.zeros((self.batch_size, self.num_steps, self.vocabulary))
        while True:
            for i in range(self.batch_size):
                if self.current_idx + self.num_steps >= len(self.data):
                    # reset the index back to the start of the data set
                    self.current_idx = 0
                x[i, :] = self.data[self.current_idx:self.current_idx + self.num_steps]
                temp_y = self.data[self.current_idx + 1:self.current_idx + self.num_steps + 1]
                # convert all of temp_y into a one hot representation
                y[i, :, :] = to_categorical(temp_y, num_classes=self.vocabulary)
                self.current_idx += self.skip_step
            yield x, y

num_steps = 30
batch_size = 20
train_data_generator = KerasBatchGenerator(train_data, num_steps, batch_size, vocabulary,
                                           skip_step=num_steps)
valid_data_generator = KerasBatchGenerator(valid_data, num_steps, batch_size, vocabulary,
                                           skip_step=num_steps)

hidden_size = 500
use_dropout=True
model = Sequential()
model.add(Embedding(vocabulary, hidden_size, input_length=num_steps))
model.add(LSTM(hidden_size, return_sequences=True))
model.add(LSTM(hidden_size, return_sequences=True))
if use_dropout:
    model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(vocabulary)))
model.add(Activation('softmax'))

optimizer = Adam()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'])

print(model.summary())
checkpointer = ModelCheckpoint(filepath=data_path + '/model-{epoch:02d}.hdf5', verbose=1)
num_epochs = 50
if args.run_opt == 1:
    model.fit_generator(train_data_generator.generate(), len(train_data)//(batch_size*num_steps), num_epochs,
                        validation_data=valid_data_generator.generate(),
                        validation_steps=len(valid_data)//(batch_size*num_steps), callbacks=[checkpointer])
    # model.fit_generator(train_data_generator.generate(), 2000, num_epochs,
    #                     validation_data=valid_data_generator.generate(),
    #                     validation_steps=10)
    model.save(data_path + "final_model.hdf5")
elif args.run_opt == 2:
    model = load_model(data_path + "\model-40.hdf5")
    dummy_iters = 40
    example_training_generator = KerasBatchGenerator(train_data, num_steps, 1, vocabulary,
                                                     skip_step=1)
    print("Training data:")
    for i in range(dummy_iters):
        dummy = next(example_training_generator.generate())
    num_predict = 10
    true_print_out = "Actual words: "
    pred_print_out = "Predicted words: "
    for i in range(num_predict):
        data = next(example_training_generator.generate())
        prediction = model.predict(data[0])
        predict_word = np.argmax(prediction[:, num_steps-1, :])
        true_print_out += reversed_dictionary[train_data[num_steps + dummy_iters + i]] + " "
        pred_print_out += reversed_dictionary[predict_word] + " "
    print(true_print_out)
    print(pred_print_out)
    # test data set
    dummy_iters = 40
    example_test_generator = KerasBatchGenerator(test_data, num_steps, 1, vocabulary,
                                                     skip_step=1)
    print("Test data:")
    for i in range(dummy_iters):
        dummy = next(example_test_generator.generate())
    num_predict = 10
    true_print_out = "Actual words: "
    pred_print_out = "Predicted words: "
    for i in range(num_predict):
        data = next(example_test_generator.generate())
        prediction = model.predict(data[0])
        predict_word = np.argmax(prediction[:, num_steps - 1, :])
        true_print_out += reversed_dictionary[test_data[num_steps + dummy_iters + i]] + " "
        pred_print_out += reversed_dictionary[predict_word] + " "
    print(true_print_out)
    print(pred_print_out)