adding the python project

tensorflow.py

@@ -0,0 +1,70 @@
+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)