CODEWITHSUNDEEP
pythontensorflow
eli44
eli44

Reputation: 150

where does the additional dimension of the Input in a keras.Model come from?

When I define a model like:

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np

input_shape = (20,20)
input = tf.keras.Input(shape=input_shape)

nn = layers.Flatten()(input)
nn = layers.Dense(10)(nn)
output = layers.Activation('sigmoid')(nn) 

model = tf.keras.Model(inputs=input, outputs=output)

Why do I need to add another dimension to my actual input:

actual_input = np.ones((1,20,20))
prediction = model.predict(actual_input)

why can't I just do actual_input = np.ones((20,20))?

Edit:

in the docs it says something about batchsize.. Is this batchsize somehow related to my question? If so, why would I need it, when I want to predict with my model? Thanks for any help.

Upvotes: 0

Views: 128

Answers (1)

Timbus Calin
Timbus Calin

Reputation: 15023

In Keras (TensorFlow), one cannot predict on a single input. Therefore, even if you have a single example, you need to add the batch_axis to it.

Practically, in this situation, you have a batch size of 1, hence the batch axis.

This is how TensorFlow and Keras are built, and even for a single prediction you need to add the batch axis (batch size of 1 == 1 single example).

You can use np.expand_dims(input,axis=0) or tf.expand_dims(input,axis=0) to transform your input into a suitable format for prediction.

Upvotes: 1

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