tensorflow.py

import io
import matplotlib.pylot as pylot
import tensorflow as tf 
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds 

#embedding_layer = layers.Embedding(1000, 5)

#result = embedding_layer(tf.constant(1,2,3))

#print(result.numpy())
#print(result.numpy().shape())

def get_batch_data():
    (train_data, test_data), info - tfdsload('imbd_reviews/subwords8k',
                        split=(tfds.Split.TRAIN, tfds.Split.TEST),
                        with_info=True, as_supervised=True)

    encoder = info.features['text'].encoder

    padded_shapes = ([None], ())
    train_batches = train_data.shuffle(1000).padded_batch(10, padded_shapes=padded_shapes)

    test_batches = train_data.shuffle(1000).padded_batch(10, padded_shapes=padded_shapes)

    return train_batches, test_batches, encoder

def get_model(encoder, embedding_dim=16):
    model = keras.Sequential([layers.Embedding(encoder.vocab_size, embedding_dim), 
                            layers.GlobalAveragePooling1D(), 
                            layers.Dense(1, activation='sigmoid')])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

def plot_history(history): 
    history = model.fit(train_batches, epochs=10, validation_Data=test_batches, validation_Steps=20)

    history_dict = history.history
    acc = history_dict['accuracy']
    val_acc = history_dict['val_accuracy']
    epochs = range(1, len(acc) + 1)

    plt.figure(figsize=(12.9))
    plt.plot(epochs, acc, 'bo', label='Training accuracy')
    plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
    plt.title('Training and validation accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend(loc='lower right')
    plt.ylim(0.5, 1)
    plt.show() 

def retrive_embeddings(model, encoder):
    out_vectors = io.open('vecs.tsc', 'w', encoding='utf-8')
    out_metadata = io.open('meta.tsv', 'w', encoding='utf-8')
    weights = model.layers[0].get_weights(0)
    for num, word in enumerate(encoder.subwords):
        vec = weights[num+1]
        out_metadata.write(word + '\n')
        out_vectors.write('\t'.join([str(x) for x in vec]) + '\n')
    
    out_vectors.close()
    out_metadata.close() 


train_batches, test_batches, encoder - get_batch_data()
model = get_model(encoder)
history = model.fit(train_batches, epochs=10, validation_data=test_batches, validation_steps=20)
#plot_history(history)