What are some common data flow operations in TensorFlow?

Common data flow operations in TensorFlow include:

1. tf.constant: defining a constant tensor.
2. tf.Variable: A tensor variable is defined.
3. tf.placeholder: defines a placeholder tensor.
4. tf.assign: assigning a value to a variable.
5. tf.add: adding tensors together.
6. tf.subtract: Subtracting tensors.
7. tf.multiply: Multiplying tensors.
8. tf.divide: dividing two tensors.
9. tf.matmul: matrix multiplication.
10. tf.reduce_sum: sum the tensor.
11. tf.reduce_mean: Calculate the average value of a tensor.
12. tf.nn.softmax: Perform a softmax operation on a tensor.
13. tf.nn.relu: Applies the ReLU activation function to a tensor.
14. tf.nn.sigmoid: applies the Sigmoid activation function to a tensor.
15. tf.nn.dropout: applying the dropout operation to a tensor.
16. tf.layers.dense: defines a fully connected layer.
17. tf.nn.conv2d: defines a two-dimensional convolutional layer.
18. tf.nn.max_pool: Defines a max pooling layer.
19. tf.nn.rnn_cell: Defines a recurrent neural network unit.
20. tf.nn.dynamic_rnn defines a dynamic RNN.