TensorFlow Operations

 TensorFlow is an open-source machine learning framework that enables the construction and execution of computational graphs for numerical computation. Operations in TensorFlow, often called ops, are nodes in the computational graph that perform mathematical functions or transformations.

Here's an overview of TensorFlow operations:

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1. Basic Operations

• Arithmetic Operations:

  • tf.add(x, y): Addition
  • tf.subtract(x, y): Subtraction
  • tf.multiply(x, y): Element-wise multiplication
  • tf.divide(x, y): Element-wise division
  • tf.math.mod(x, y): Modulus
  • tf.pow(x, y): Element-wise power
  • tf.sqrt(x): Square root

• Reduction Operations:

  • tf.reduce_sum(input_tensor, axis=None): Sum of elements
  • tf.reduce_mean(input_tensor, axis=None): Mean of elements
  • tf.reduce_max(input_tensor, axis=None): Maximum value
  • tf.reduce_min(input_tensor, axis=None): Minimum value
  • tf.reduce_prod(input_tensor, axis=None): Product of elements

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2. Matrix Operations

• Matrix Multiplication:

  • tf.matmul(a, b): Matrix product
  • tf.linalg.matmul(a, b): More advanced matrix multiplication

• Transpose and Inverse:

  • tf.transpose(a, perm=None): Transposes a tensor
  • tf.linalg.inv(a): Inverse of a matrix

• Eigenvalues and Decomposition:

  • tf.linalg.eig(a): Eigenvalues and eigenvectors
  • tf.linalg.svd(a): Singular Value Decomposition

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3. Tensor Manipulation

• Shape and Reshape:

  • tf.shape(input): Returns the shape of the tensor
  • tf.reshape(tensor, shape): Reshapes a tensor

• Concatenation and Splitting:

  • tf.concat(values, axis): Concatenates tensors along a specific axis
  • tf.split(value, num_or_size_splits, axis=0): Splits a tensor

• Slicing and Indexing:

  • tf.slice(input_, begin, size): Extracts a slice
  • tf.gather(params, indices, axis=0): Gathers elements from indices

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4. Neural Network Operations

• Activation Functions:

  • tf.nn.relu(features): Rectified Linear Unit
  • tf.nn.sigmoid(x): Sigmoid function
  • tf.nn.tanh(x): Hyperbolic tangent
  • tf.nn.softmax(logits): Softmax function

• Convolution:

  • tf.nn.conv2d(input, filters, strides, padding): 2D convolution
  • tf.nn.depthwise_conv2d(input, filter, strides, padding): Depthwise convolution

• Pooling:

  • tf.nn.max_pool(input, ksize, strides, padding): Max pooling
  • tf.nn.avg_pool(input, ksize, strides, padding): Average pooling

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5. Random Operations

• Random Sampling:
  • tf.random.normal(shape, mean=0.0, stddev=1.0): Samples from a normal distribution
  • tf.random.uniform(shape, minval=0, maxval=None): Samples from a uniform distribution
  • tf.random.categorical(logits, num_samples): Draws samples from a categorical distribution

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6. Loss Functions

• tf.losses.mean_squared_error(y_true, y_pred): Mean Squared Error
• tf.losses.sparse_categorical_crossentropy(y_true, y_pred): Sparse Categorical Crossentropy
• tf.losses.binary_crossentropy(y_true, y_pred): Binary Crossentropy

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7. Gradient Computation

• Automatic Differentiation:

  • with tf.GradientTape() as tape: Computes gradients with respect to variables or inputs

• Gradient Operations:

  • tape.gradient(target, sources): Computes the gradient of the target with respect to the sources

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8. Control Flow Operations

• tf.cond(pred, true_fn, false_fn): Conditional branching
• tf.while_loop(cond, body, loop_vars): Loop operation

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TensorFlow also supports a range of specialized ops for tasks like image processing (tf.image), sequence handling (tf.rnn), and reinforcement learning. Let me know if you need detailed examples or specific operations!