dynet38

import dynet as dy import random # Initialize DyNet (mandatory) - can specify options like --dynet-gpus, --dynet-mem dy.init() # Create a model (parameter collection) to hold network weights m = dy.ParameterCollection() # Add parameters for a simple feedforward layer # W: weight matrix (32 output units, 10 input units) # b: bias vector (32 output units) # V: output weight matrix (1 output unit, 32 input units) W = m.add_parameters((32, 10)) b = m.add_parameters(32) V = m.add_parameters((1, 32)) # Define a function to build the computation graph for a given input def build_graph(x_val): # Renew the computation graph for each new example dy.renew_cg() # Convert Python list (input vector) to DyNet Expression x = dy.inputVector(x_val) # Get parameters as DyNet Expressions for the current graph W_expr = dy.parameter(W) b_expr = dy.parameter(b) V_expr = dy.parameter(V) # Perform operations to build the graph: tanh activation, logistic output h = dy.tanh(W_expr * x + b_expr) o = dy.logistic(V_expr * h) return o # Example usage with random input data input_data = [random.random() for _ in range(10)] output_expression = build_graph(input_data) # Get the scalar value from the output expression output_value = output_expression.value() print(f"Input (first 5 values): {[f'{v:.2f}' for v in input_data[:5]]}...") print(f"Predicted Output: {output_value[0]:.4f}") # For training, you would then get loss, call backward(), and update with a Trainer. # Example (not run here): # trainer = dy.SimpleSGDTrainer(m) # loss = dy.sum_batches(dy.square(output_expression - dy.scalarInput(target_value))) # loss.backward() # trainer.update()