Add Keras Tuner CIFAR10 example

examples/frameworks/kerastuner/keras_tuner_cifar.py

@@ -0,0 +1,74 @@
+"""Keras Tuner CIFAR10 example for the TensorFlow blog post."""
+
+import kerastuner as kt
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from trains.external.kerastuner import TrainsTunerLogger
+
+from trains import Task
+
+physical_devices = tf.config.list_physical_devices('GPU')
+if physical_devices:
+    tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+
+def build_model(hp):
+    inputs = tf.keras.Input(shape=(32, 32, 3))
+    x = inputs
+    for i in range(hp.Int('conv_blocks', 3, 5, default=3)):
+        filters = hp.Int('filters_' + str(i), 32, 256, step=32)
+        for _ in range(2):
+            x = tf.keras.layers.Convolution2D(
+              filters, kernel_size=(3, 3), padding='same')(x)
+            x = tf.keras.layers.BatchNormalization()(x)
+            x = tf.keras.layers.ReLU()(x)
+        if hp.Choice('pooling_' + str(i), ['avg', 'max']) == 'max':
+            x = tf.keras.layers.MaxPool2D()(x)
+        else:
+            x = tf.keras.layers.AvgPool2D()(x)
+    x = tf.keras.layers.GlobalAvgPool2D()(x)
+    x = tf.keras.layers.Dense(
+      hp.Int('hidden_size', 30, 100, step=10, default=50),
+      activation='relu')(x)
+    x = tf.keras.layers.Dropout(
+      hp.Float('dropout', 0, 0.5, step=0.1, default=0.5))(x)
+    outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
+
+    model = tf.keras.Model(inputs, outputs)
+    model.compile(
+      optimizer=tf.keras.optimizers.Adam(
+        hp.Float('learning_rate', 1e-4, 1e-2, sampling='log')),
+      loss='sparse_categorical_crossentropy',
+      metrics=['accuracy'])
+    return model
+
+
+task = Task.init('examples', 'kerastuner cifar10 tuning')
+
+tuner = kt.Hyperband(
+    build_model,
+    project_name='kt examples',
+    logger=TrainsTunerLogger(),
+    objective='val_accuracy',
+    max_epochs=10,
+    hyperband_iterations=6)
+
+data = tfds.load('cifar10')
+train_ds, test_ds = data['train'], data['test']
+
+
+def standardize_record(record):
+    return tf.cast(record['image'], tf.float32) / 255., record['label']
+
+
+train_ds = train_ds.map(standardize_record).cache().batch(64).shuffle(10000)
+test_ds = test_ds.map(standardize_record).cache().batch(64)
+
+tuner.search(train_ds,
+             validation_data=test_ds,
+             callbacks=[tf.keras.callbacks.EarlyStopping(patience=1),
+                        tf.keras.callbacks.TensorBoard(),
+                        ])
+
+best_model = tuner.get_best_models(1)[0]
+best_hyperparameters = tuner.get_best_hyperparameters(1)[0]

examples/frameworks/kerastuner/requirements.txt

@@ -0,0 +1,3 @@
+keras-tuner
+tensorflow>=2.0
+tensorflow-datasets

examples/frameworks/pytorch/manual_model_upload.py

@@ -39,5 +39,5 @@ task.connect_label_enumeration(labels)
 # storing the model, it will have the task network configuration and label enumeration
 print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
 
-torch.save(model, os.path.join(gettempdir(), "model"))
+torch.save(model, os.path.join(gettempdir(), "model.pt"))
 print('Model saved')

examples/reporting/artifacts.py

@@ -40,7 +40,6 @@ task.upload_artifact('pillow_image', im)
 task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))
 # add and upload a wildcard
 task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
-
 # do something here
 sleep(1.)
 print(df)