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  1. tensorflow.org

    tf.shape returns a 1-D integer tensor representing the shape of input. Learn how to use tf.shape with scalar, symbolic, and sparse tensors, and see examples and related functions.
    • Introduction to Tensors

      <tf.Tensor&colon; shape=(), dtype=int64, numpy=3> About shapes. Tensors have shapes. Some vocabulary: Shape: The length (number of elements) of each of the axes of a tensor. Rank: Number of tensor axes. A scalar has rank 0, a vector has rank 1, a matrix is rank 2. Axis or Dimension: A particular dimension of a tensor.

  2. tensorflow.org

    Aug 15, 2024<tf.Tensor&colon; shape=(), dtype=int64, numpy=3> About shapes. Tensors have shapes. Some vocabulary: Shape: The length (number of elements) of each of the axes of a tensor. Rank: Number of tensor axes. A scalar has rank 0, a vector has rank 1, a matrix is rank 2. Axis or Dimension: A particular dimension of a tensor.
  3. Jun 12, 2024Tensor("Const_6:0", shape=(1, 3, 2), dtype=int16) The matrix looks like the picture two. Shape of tensor. When you print tensor, TensorFlow guesses the shape. However, you can get the shape of the tensor with the TensorFlow shape property. Below, you construct a matrix filled with a number from 10 to 15 and you check the shape of m_shape
  4. slingacademy.com

    Dec 21, 2024Manipulating tensor shapes is not merely theoretical; it finds extensive utility in real-world applications. For example, image data is typically treated as tensors where dimensions might represent data such as height, width, and color channels. Being able to easily confirm and adjust these tensor arrays aids smooth training and model optimization.
  5. slingacademy.com

    Dec 20, 2024Static shapes are used for compile-time checks by assessing the shape of tensors directly through .shape which is useful for debugging and model prototyping. Dynamic shape graphs use tf.shape() , providing flexibility especially where tensors dimensions may vary, which is often the case in production systems.
  6. stackoverflow.com

    Use tf.shape(tensor)[0] to get a scalar tensor with the variable dimension. It is useful if you then need to reshape. tensor.get_shape().as_list()[0] generates None for shapes with (?, ... ). This is usually the position for the batch size when training models. Reference: TF Issues
  7. the .set_shape method simply assigns to the .shape property of the tensor the specified value.. In this way, the definition of all the convolutional layer layer1, layer2 and encode can succeed. Let's analyze the shapes of the layer1 (the same reasoning applies for every convolutional layer in the network):. Convolutional layer shapes. At graph definition time we know the input depth 3, this ...
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