Merge pull request #827 from Kaggle/add-keras-tuner

Install keras-tuner

Author: rosbo

Dockerfile

@@ -88,6 +88,7 @@ RUN apt-get install -y libfreetype6-dev && \
     pip install lightgbm==2.3.1 && \
     pip install git+git://github.com/Lasagne/Lasagne.git && \
     pip install keras && \
+    pip install keras-tuner && \
     pip install flake8 && \
     #neon
     cd /usr/local/src && \

tests/test_keras_tuner.py

@@ -0,0 +1,35 @@
+import unittest
+
+import tensorflow as tf
+import numpy as np
+
+from kerastuner.tuners import RandomSearch
+
+
+class TestKerasTuner(unittest.TestCase):
+    def test_search(self):
+        def build_model(hp):
+            x_train = np.random.random((100, 28, 28))
+            y_train = np.random.randint(10, size=(100, 1))
+            x_test = np.random.random((20, 28, 28))
+            y_test = np.random.randint(10, size=(20, 1))
+
+            model = tf.keras.models.Sequential([
+                tf.keras.layers.Flatten(input_shape=(28, 28)),
+                tf.keras.layers.Dense(128, activation='relu'),
+                tf.keras.layers.Dropout(hp.Choice('dropout_rate', values=[0.2, 0.4])),
+                tf.keras.layers.Dense(10, activation='softmax')
+            ])
+
+            model.compile(
+                optimizer='adam',
+                loss='sparse_categorical_crossentropy',
+                metrics=['accuracy'])
+
+            return model
+
+            tuner = RandomSearch(build_model, objective='accuracy', max_trials=1, executions_per_trial=1, seed=1)
+
+            tuner.search(x_train, y_train, epochs=1)
+
+            self.assertEqual(0.4, tuner.get_best_hyperparameters(1)[0].get('dropout_rate'))