Inputtohidden

Inputtohidden, sometimes written as input-to-hidden, refers to the set of connections that link the input layer to the hidden layer in a neural network. These connections are captured by a weight matrix that transforms input features into the activations of hidden units, enabling the network to learn abstract representations.

In a standard feedforward network, the hidden layer activations h are computed as h = f(W1 x + b1), where W1 is the input-to-hidden weight matrix, x is the input vector, b1 is a bias vector, and f is a nonlinear activation function. The dimensions of W1 are typically (hidden_size, input_size), reflecting how each hidden unit receives input from all input features.

In recurrent neural networks, there is a related concept of input-to-hidden weights, often denoted W_xh or W_x, which multiply the input at each time step. These weights are combined with hidden-to-hidden weights W_hh to produce the hidden state, enabling the network to process sequences. In this context, inputtohidden is static across time steps, while hidden-to-hidden terms carry temporal information.

Initialization and training are critical. Weights are usually initialized with small random values (e.g., Xavier/Glorot or He initialization) and learned through backpropagation. In recurrent models, training uses backpropagation through time, which can be affected by vanishing or exploding gradients, making proper initialization and regularization important.

Overall, inputtohidden connections are fundamental to how neural networks extract features from raw inputs and form the basis for subsequent processing in deeper layers or output generation.