I'm using Talos and Google colab TPU to run hyperparameter tuning of a Keras model. Note that I'm using Tensorflow 1.15.0 and Keras 2.2.4-tf.
import os
import tensorflow as tf
import talos as ta
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
def iris_model(x_train, y_train, x_val, y_val, params):
# Specify a distributed strategy to use TPU
resolver = tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.contrib.distribute.initialize_tpu_system(resolver)
strategy = tf.contrib.distribute.TPUStrategy(resolver)
# Use the strategy to create and compile a Keras model
with strategy.scope():
model = Sequential()
model.add(Dense(32, input_shape=(4,), activation=tf.nn.relu, name="relu"))
model.add(Dense(3, activation=tf.nn.softmax, name="softmax"))
model.compile(optimizer=Adam(learning_rate=0.1), loss=params['losses'])
# Convert data type to use TPU
x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.cache()
dataset = dataset.shuffle(1000, reshuffle_each_iteration=True).repeat()
dataset = dataset.batch(params['batch_size'], drop_remainder=True)
# Fit the Keras model on the dataset
out = model.fit(dataset, batch_size=params['batch_size'], epochs=params['epochs'], validation_data=[x_val, y_val], verbose=0, steps_per_epoch=2)
return out, model
# Load dataset
X, y = ta.templates.datasets.iris()
# Train and test set
x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.30, shuffle=False)
# Create a hyperparameter distributions
p = {'losses': ['logcosh'], 'batch_size': [128, 256, 384, 512, 1024], 'epochs': [10, 20]}
# Use Talos to scan the best hyperparameters of the Keras model
scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)
After converting the train set to a Dataset using tf.data.Dataset, I get the following error when fitting the model with out = model.fit:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-c812209b95d0> in <module>()
8
9 # Use Talos to scan the best hyperparameters of the Keras model
---> 10 scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)
8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in _validate_or_infer_batch_size(self, batch_size, steps, x)
1813 'The `batch_size` argument must not be specified for the given '
1814 'input type. Received input: {}, batch_size: {}'.format(
-> 1815 x, batch_size))
1816 return
1817
ValueError: The `batch_size` argument must not be specified for the given input type. Received input: <DatasetV1Adapter shapes: ((512, 4), (512, 3)), types: (tf.float32, tf.float32)>, batch_size: 512
Then, if I follow those instructions and don't set the batch-size argument to model.fit. I get another error in:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-c812209b95d0> in <module>()
8
9 # Use Talos to scan the best hyperparameters of the Keras model
---> 10 scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)
8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in _distribution_standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, validation_split, shuffle, epochs, allow_partial_batch)
2307 strategy) and not drop_remainder:
2308 dataset_size = first_x_value.shape[0]
-> 2309 if dataset_size % batch_size == 0:
2310 drop_remainder = True
2311
TypeError: unsupported operand type(s) for %: 'int' and 'NoneType'