Creating a TensorFlow placeholder for 3D Images

Python TensorFlow Building and Training a Simple Model: Exercise-5 with Solution

Write a Python program that creates a TensorFlow placeholder for a 3D tensor representing images with dimensions (batch_size, height, width).

Sample Solution:

Python Code:

import tensorflow as tf

# Define the shape of the input tensor
batch_size = None  # Variable batch size
height = 128  # Height of the images
width = 128   # Width of the images

# Create a TensorFlow input layer for images
input_images = tf.keras.layers.Input(shape=(height, width), batch_size=batch_size, dtype=tf.float32)

# Print the input layer (placeholder)
print("Input Placeholder (Tensor):", input_images)

Explanation:

In the exercise above -

• Import the necessary modules.
• Define the input tensor shape:
• batch_size is set to None to indicate a variable batch size. You can specify a specific batch size if needed.
• height and width represent the height and width dimensions of the images.
• Create a TensorFlow input layer for images using tf.keras.layers.Input. We specify the shape argument to set the shape of the input, and we set batch_size=batch_size to indicate that the batch size can vary. Additionally, we specify the input data type as tf.float32.
• Finally, we print the input layer, which serves as a placeholder for image data.

Output:

Input Placeholder (Tensor): KerasTensor(type_spec=TensorSpec(shape=(None, 128, 128), dtype=tf.float32, name='input_2'), name='input_2', description="created by layer 'input_2'")

Explanation(Output):

In the exercise above -

• Input Placeholder (Tensor): Indicate the following information describes an input placeholder tensor.
• KerasTensor(type_spec=TensorSpec(shape=(None, 128, 128), dtype=tf.float32, name='input_2'): This part provides detailed information about the input placeholder tensor:
• KerasTensor: This indicates that it's a tensor object created using Keras, which is a high-level API within TensorFlow.
• type_spec=TensorSpec(...): This section specifies the type specification of the tensor, including its shape, data type, and name.
• shape=(None, 128, 128): This indicates the tensor shape. In this case, it's a 3D tensor with dimensions (batch_size, 128, 128). The use of None in the shape means that the tensor can have a variable batch size (batch_size is not fixed), but it has a fixed size of 128 in both the second and third dimensions, representing the height and width of the images.
• dtype=tf.float32: Specifies the tensor data type, which is tf.float32. It means the tensor contains 32-bit floating-point values.
• name='input_2': This is the name assigned to the tensor, which is 'input_2'. Names are often used to identify tensors within a computational graph.
• name='input_2', description="created by layer 'input_2'": Information about the name and origin of the tensor:
• name='input_2': Repeats the name of the tensor, which is 'input_2'.
• description="created by layer 'input_2'": This indicates that the tensor was created by a layer named 'input_2'. This is helpful for tracking the tensor source within a neural network model.

Python Code Editor:

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