Merge pull request #125 from jkhenning/master

Add autokeras example

examples/autokeras/autokeras_imdb_example.py

@@ -0,0 +1,53 @@
+import autokeras as ak
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from trains import Task
+
+task = Task.init(project_name="autokeras", task_name="autokeras imdb example with scalars")
+
+def imdb_raw():
+    max_features = 20000
+    index_offset = 3  # word index offset
+
+    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(
+        num_words=max_features,
+        index_from=index_offset)
+    x_train = x_train
+    y_train = y_train.reshape(-1, 1)
+    x_test = x_test
+    y_test = y_test.reshape(-1, 1)
+
+    word_to_id = tf.keras.datasets.imdb.get_word_index()
+    word_to_id = {k: (v + index_offset) for k, v in word_to_id.items()}
+    word_to_id["<PAD>"] = 0
+    word_to_id["<START>"] = 1
+    word_to_id["<UNK>"] = 2
+
+    id_to_word = {value: key for key, value in word_to_id.items()}
+    x_train = list(map(lambda sentence: ' '.join(
+        id_to_word[i] for i in sentence), x_train))
+    x_test = list(map(lambda sentence: ' '.join(
+        id_to_word[i] for i in sentence), x_test))
+    x_train = np.array(x_train, dtype=np.str)
+    x_test = np.array(x_test, dtype=np.str)
+    return (x_train, y_train), (x_test, y_test)
+
+
+# Prepare the data.
+(x_train, y_train), (x_test, y_test) = imdb_raw()
+print(x_train.shape)  # (25000,)
+print(y_train.shape)  # (25000, 1)
+print(x_train[0][:50])  # <START> this film was just brilliant casting <UNK>
+
+# Initialize the TextClassifier
+clf = ak.TextClassifier(max_trials=3)
+
+tensorboard_callback_train = keras.callbacks.TensorBoard(log_dir='log')
+tensorboard_callback_test = keras.callbacks.TensorBoard(log_dir='log')
+
+# Search for the best model.
+clf.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback_train])
+clf.fit(x_test, y_test, epochs=2, callbacks=[tensorboard_callback_test])
+# Evaluate on the testing data.
+print('Accuracy: {accuracy}'.format(accuracy=clf.evaluate(x_test, y_test)))