Fix Kerastuner framework and examples #1279

examples/frameworks/kerastuner/keras_tuner_cifar.py

@@ -3,75 +3,77 @@
 import keras_tuner as kt
 import tensorflow as tf
 import tensorflow_datasets as tfds
-from clearml.external.kerastuner import ClearmlTunerLogger
+
+from clearml.external.kerastuner import ClearmlTunerCallback
 
 from clearml import Task
 
-physical_devices = tf.config.list_physical_devices('GPU')
+physical_devices = tf.config.list_physical_devices("GPU")
 if physical_devices:
-    tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')
+    tf.config.experimental.set_visible_devices(physical_devices[0], "GPU")
     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 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.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':
+        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.AvgPool2D(pool_size=1)(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)
+    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'])
+        optimizer=tf.keras.optimizers.Adam(hp.Float("learning_rate", 1e-4, 1e-2, sampling="log")),
+        loss="sparse_categorical_crossentropy",
+        metrics=["accuracy"],
+    )
     return model
 
 
 # Connecting ClearML with the current process,
 # from here on everything is logged automatically
-task = Task.init('examples', 'kerastuner cifar10 tuning')
+task = Task.init("examples", "kerastuner cifar10 tuning")
 
 tuner = kt.Hyperband(
     build_model,
-    project_name='kt examples',
-    logger=ClearmlTunerLogger(),
-    objective='val_accuracy',
+    project_name="kt examples",
+    # logger=ClearmlTunerLogger(),
+    objective="val_accuracy",
     max_epochs=10,
-    hyperband_iterations=6)
+    hyperband_iterations=6,
+)
 
-data = tfds.load('cifar10')
-train_ds, test_ds = data['train'], data['test']
+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']
+    return tf.cast(record["image"], tf.float32) / 255.0, 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(),
-                        ])
+tuner.search(
+    train_ds,
+    validation_data=test_ds,
+    callbacks=[
+        tf.keras.callbacks.EarlyStopping(patience=1),
+        tf.keras.callbacks.TensorBoard(),
+        ClearmlTunerCallback(tuner)
+    ],
+)
 
 best_model = tuner.get_best_models(1)[0]
 best_hyperparameters = tuner.get_best_hyperparameters(1)[0]

examples/frameworks/kerastuner/keras_tuner_cifar_legacy.py

@@ -0,0 +1,77 @@
+"""Keras Tuner CIFAR10 example for the TensorFlow blog post."""
+
+import keras_tuner as kt
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from clearml.external.kerastuner import ClearmlTunerLogger
+
+from clearml import Task
+
+physical_devices = tf.config.list_physical_devices('GPU')
+if physical_devices:
+    tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')
+    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
+
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
+task = Task.init('examples', 'kerastuner cifar10 tuning')
+
+tuner = kt.Hyperband(
+    build_model,
+    project_name='kt examples',
+    logger=ClearmlTunerLogger(),
+    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]