CODEWITHSUNDEEP
pythontensorflowkeras
Kshitiz Sharma
Kshitiz Sharma

Reputation: 18597

Tensorflow: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data

Consider the following TensorFlow code:

import numpy as np
import tensorflow as tf

import tensorflow_datasets as tfds

mnist_dataset, mnist_info = tfds.load(name = 'mnist', with_info=True, as_supervised=True)

mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']

num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples
num_validation_samples = tf.cast(num_validation_samples, tf.int64)

num_test_samples = mnist_info.splits['test'].num_examples
num_test_samples = tf.cast(num_test_samples, tf.int64)

def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255.
    return image, label

scaled_train_and_validation_data = mnist_train.map(scale)
test_data = mnist_test.map(scale)

BUFFER_SIZE = 10_000

shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)

validation_data = shuffled_train_and_validation_data.take(num_validation_samples)
train_data = shuffled_train_and_validation_data.skip(num_validation_samples)

BATCH_SIZE = 100
train_data = train_data.batch(BATCH_SIZE)
validation_data = validation_data.batch(num_validation_samples) # Single batch, having size equal to number of validation samples
test_data = test_data.batch(num_test_samples)

validation_inputs, validation_targets = next(iter(validation_data))

input_size = 784 # One for each pixel of the 28 * 28 image
output_size = 10
hidden_layer_size = 50 # Arbitrary chosen

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,28,1)),
    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # First hidden layer
    tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
    tf.keras.layers.Dense(output_size, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
NUM_EPOCHS = 5
model.fit(train_data, epochs = NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2)

On running it tf gives the error:

ValueError: batch_size or steps is required for Tensor or NumPy input data.

When batch_size is added in the call to fit():

model.fit(train_data, batch_size = BATCH_SIZE, epochs = NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2)

It then complains:

ValueError: The batch_size argument must not be specified for the given input type. Received input: , batch_size: 100

What is the error here?

Upvotes: 14

Views: 3173

Answers (3)

Akshat_Arv
Akshat_Arv

Reputation: 11

If you visit this link - https://www.tensorflow.org/api_docs/python/tf/keras/Model, you will find that fit() requires the argument - validation_steps only if validation_data is provided and is a tf.data dataset. As in your code, it looks like you created the validation_data by splitting the training part of dataset.

Upvotes: 1

Agnibha Bhattacharjya
Agnibha Bhattacharjya

Reputation: 21

NUM_EPOCHS=5
    model.fit(train_data,epochs= NUM_EPOCHS,
    validation_data=(validation_inputs, validation_targets),
    validation_steps=10,verbose=2)

Upvotes: 2

rvinas
rvinas

Reputation: 11895

The error happens because a tf.Dataset is provided to the argument validation_data of Model.fit, but Keras does not know how many steps to validate for. To solve this problem, you can just set the argument validation_steps. For example:

model.fit(train_data,
    batch_size=BATCH_SIZE,
    epochs=NUM_EPOCHS,
    validation_data=(validation_inputs, validation_targets),
    validation_steps=10)

Upvotes: 13

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