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hadifar

import tensorflow as tf

tf.enable_eager_execution()

file_name = 'sentiment.csv'
batch_size = 8
num_buckets = 4
output_buffer_size = batch_size * 1000
reshuffle_each_iteration = True
random_seed = 1023

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def __call__(self, inputs):
    emb = self.embed(inputs)
    d1 = self.dense1(emb)
    d1 = self.global_pooling(d1)
    outputs = self.pred(d1)
    return outputs

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self.global_pooling = tf.keras.layers.GlobalAveragePooling1D()

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self.dense1 = tf.keras.layers.Dense(units=hid_size, activation=tf.nn.relu)

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self.embed = tf.keras.layers.Embedding(vocab_size, 128)

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table = tf.contrib.lookup.index_table_from_file(vocabulary_file="vocab.txt", num_oov_buckets=1)
csv_dataset = csv_dataset.map(lambda x, y: (tf.string_split([x]).values, y))
csv_dataset = csv_dataset.map(lambda x, y: (tf.cast(table.lookup(x), tf.int32), y))

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csv_dataset = csv_dataset.map(lambda x, y: (x, tf.one_hot(y, 2)))

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# convert string labels into numeric value
csv_dataset = csv_dataset.map(lambda x, y: (x, tf.cond(tf.equal(y, 'positive'), lambda: 1, lambda: 0)))

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# change label-sentence to sentence-label
csv_dataset = csv_dataset.map(lambda x, y: (y, x))