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'
from_tensor_slices creates a dataset with a separate element for each row of the input tensor: >>> t = tf.constant([[1, 2], [3, 4]]) >>> ds = tf.data.Dataset.from_tensor_slices(t) >>> [x for x in ds] [<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>, <tf.Tensor: shape=(2,), dtype=int32, numpy= ...
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf. data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets.
To get the shape of a tensor, you can easily use the tf. shape() function. This method will help the user to return the shape of the given tensor.
Dataset. prefetch transformation. It can be used to decouple the time when data is produced from the time when data is consumed. In particular, the transformation uses a background thread and an internal buffer to prefetch elements from the input dataset ahead of the time they are requested.
It looks to me that the problem with your code is that the training and validation data is not in the same format. You are batching the training data but not the validation examples.
You can ensure that they are in the same format by replacing the
bottom half of your iris_model
function with this:
def fix_data(x, y):
x = x.astype('float32')
ds = Dataset.from_tensor_slices((x, y))
ds = ds.cache()
ds = ds.shuffle(1000, reshuffle_each_iteration = True)
ds = ds.repeat()
ds = ds.batch(params['batch_size'], drop_remainder = True)
return ds
train = fix_data(x_train, y_train)
val = fix_data(x_val, y_val)
# Fit the Keras model on the dataset
out = model.fit(x = train, epochs = params['epochs'],
steps_per_epoch = 2,
validation_data = val,
validation_steps = 2)
At least this works for me and your code runs without error.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With