task(ium_06) Use of Tensorflow

main2.py

@@ -0,0 +1,59 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from tensorflow.keras import layers
+import numpy as np
+import tensorflow as tf
+
+
+def normalization(label):
+    return 0 if label == False else 1
+
+
+def main():
+    data = pd.read_csv('Amazon_Consumer_Reviews.csv', header=0, sep=',')
+    column_names = ['reviews.doRecommend', 'reviews.title']
+    data = data[column_names]
+
+    data_train, data_test = train_test_split(data, train_size=0.6, random_state=1)
+    data_test, data_val = train_test_split(data_test, test_size=0.5, random_state=1)
+
+    train_labels = [normalization(x) for x in np.array(data_train['reviews.doRecommend'])]
+    train_examples = np.array(data_train['reviews.title'])
+    test_examples = np.array(data_test['reviews.title'])
+    test_labels = [normalization(x) for x in np.array(data_test['reviews.doRecommend'])]
+    val_labels = [normalization(x) for x in np.array(data_val['reviews.doRecommend'])]
+    val_examples = np.array(data_val['reviews.title'])
+
+    # print("Training entries: {}, test entries: {}".format(len(data_train), len(data_test)))
+    # print(train_examples)
+    # print(train_labels)
+
+    model = tf.keras.Sequential([
+        layers.Input(shape=(12,)),
+        layers.Dense(32),
+        layers.Dense(16),
+        layers.Dense(2, activation='softmax')
+    ])
+
+    model.summary()
+
+    model.compile(
+        loss=tf.losses.BinaryCrossentropy(),
+        optimizer=tf.optimizers.Adam(),
+        metrics=[tf.keras.metrics.BinaryAccuracy()])
+
+    history = model.fit(train_examples, train_labels,
+                        epochs=40,
+                        batch_size=512,
+                        validation_data=(val_examples, val_labels),
+                        verbose=1)
+
+    results = model.evaluate(test_examples, test_labels)
+
+    file = open('results.txt', 'w')
+    file.write('test loss: ' + str(results[0]) + '\n' + 'test accuracy: ' + str(results[1]))
+    file.close()
+
+
+if __name__ == '__main__':
+    main()