TensorFlow common methods

TensorFlow common methods:

Notes:

tf refers to the tensorflow data structure, in this guide we present the TensorFlow common methods

Extracting Values

• Indexing: 

tf[index]: Extracts elements from a tensor using indices.

• Transforms data type tensorflow

tf.numpy().tolist(): Transforms data ype into python list

tf.numpy(): Transforms data type into numpy array

• TensorFlow Operations:

tf.gather: Extracts elements from a tensor based on indices.

tf.slice: Extracts a slice of a tensor.

tf.boolean_mask: Extracts elements based on a boolean mask.

tf.reduce_max, tf.reduce_min, tf.reduce_mean: Computes aggregate values over the tensor.

Arithmetic Operations

• Addition: 

tf.add(x, y): Adds two tensors element-wise.

• Subtraction: 

tf.subtract(x, y): Subtracts one tensor from another element-wise.

• Multiplication: 

tf.multiply(x, y): Multiplies two tensors element-wise.

• Division: 

tf.divide(x, y): Divides one tensor by another element-wise.

• Power: 

tf.pow(x, y): Raises a tensor to a power element-wise.

• Square Root: 

tf.sqrt(x): Calculates the square root of each element in a tensor.

• Absolute Value: 

tf.abs(x): Calculates the absolute value of each element in a tensor.

• Negation: 

tf.negative(x): Negates each element in a tensor.

Matrix Operations

• Matrix Multiplication: 

tf.matmul(x, y): Multiplies two matrices.

• Transpose: 

tf.transpose(x): Transposes a tensor.

• Determinant: 

tf.linalg.det(x): Calculates the determinant of a square matrix.

• Inverse: 

tf.linalg.inv(x): Calculates the inverse of a square matrix.

• Trace:

tf.linalg.trace(x): Calculates the trace of a matrix.

• Eigenvalues:

 tf.linalg.eigvals(x): Calculates the eigenvalues of a matrix.

Activation Functions

• Sigmoid: 

tf.sigmoid(x): Applies the sigmoid activation function.

• ReLU: 

tf.nn.relu(x): Applies the rectified linear unit (ReLU) activation function.

• Tanh: 

tf.tanh(x): Applies the hyperbolic tangent activation function.

• Softmax: 

tf.nn.softmax(x): Applies the softmax activation function, often used for classification.

• Leaky ReLU: 

tf.nn.leaky_relu(x): Applies the leaky ReLU activation function.

• ELU: 

tf.nn.elu(x): Applies the exponential linear unit (ELU) activation function.

Loss Functions

• Mean Squared Error: 

tf.keras.losses.mean_squared_error(y_true, y_pred): Calculates the mean squared error between predicted and true values.

• Cross-Entropy: 

tf.keras.losses.categorical_crossentropy(y_true, y_pred): Calculates the categorical cross-entropy loss.

• Binary Cross-Entropy: 

tf.keras.losses.binary_crossentropy(y_true, y_pred): Calculates the binary cross-entropy loss.

• Hinge Loss: 

tf.keras.losses.hinge(y_true, y_pred): Calculates the hinge loss, often used in support vector machines.

• Cosine Similarity: 

tf.keras.losses.cosine_similarity(y_true, y_pred): Calculates the cosine similarity between two vectors.

Optimization Algorithms

• Gradient Descent: 

tf.keras.optimizers.SGD(): Implements the stochastic gradient descent optimization algorithm.

• Adam: 

tf.keras.optimizers.Adam(): Implements the Adam optimization algorithm.

• RMSprop: 

tf.keras.optimizers.RMSprop(): Implements the RMSprop optimization algorithm.

• Adagrad: 

tf.keras.optimizers.Adagrad(): Implements the Adagrad optimization algorithm.

Neural Network Layers

• Dense: 

tf.keras.layers.Dense(): Creates a fully connected layer.

• Convolutional: 

tf.keras.layers.Conv2D(): Creates a 2D convolutional layer.

• Recurrent: 

tf.keras.layers.LSTM(): Creates a long short-term memory (LSTM) layer.

• Pooling: 

tf.keras.layers.MaxPooling2D(): Creates a 2D max pooling layer.

• Dropout: 

tf.keras.layers.Dropout(): Applies dropout regularization.

• Batch Normalization: 

tf.keras.layers.BatchNormalization(): Applies batch normalization.

Data Manipulation

• Reshaping: 

tf.reshape(x, shape): Reshapes a tensor.

• Slicing: 

tf.slice(x, begin, size): Extracts a slice from a tensor.

• Concatenation: 

tf.concat(values, axis): Concatenates tensors along a specified axis.

• Stacking: 

tf.stack(values, axis): Stacks tensors along a new axis.

• Tile: 

tf.tile(x, multiples): Repeats a tensor multiple times.

• Padding: 

tf.pad(x, paddings): Pads a tensor with zeros.

Miscellaneous

• Reduce Mean: 

tf.reduce_mean(x): Calculates the mean of a tensor.

• Reduce Sum: 

tf.reduce_sum(x): Calculates the sum of a tensor.

• Reduce Max: 

tf.reduce_max(x): Calculates the maximum value in a tensor.

• Reduce Min: 

tf.reduce_min(x): Calculates the minimum value in a tensor.

• Cast:

 tf.cast(x, dtype): Casts a tensor to a different data type.

• Shape: 

tf.shape(x): Returns the shape of a tensor.

• Size: 

tf.size(x): Returns the number of elements in a tensor.

• Random: 

tf.random.uniform(shape): Generates random numbers from a uniform distribution.