from libraries import Activation_Softmax from libraries import Loss_CategoricalCrossentropy from libraries import Activation_ReLU from libraries import Layer_Dense # initialize nnfs dataset nnfs.init() # Create dataset x, y = spiral_data(samples=100, classes=3) # Create Dense Layer with 2 input features and 3 output values dense1 = Layer_Dense(2, 3) # Create ReLU activation (to be used with Dense Layer) activation1_relu = Activation_ReLU() # Create second Dense layer with 3 input features (as we take ouput of previous layer here) # and 3 output values (output values) dense2 = Layer_Dense(3, 3) # Create a softmax classfier's combined loss and activation loss_activation = Activation_Softmax_Loss_CategoricalCorssentropy() # Perform a forward pass of our training data through this layer dense1.forward(x) # Perform a forward pass through activation function # takes the output of first dense layer here activation1_relu.forward(dense1.output)
from libraries import Layer_Dense from libraries import Activation_ReLU from libraries import Activation_Softmax # Initializes NNFS nnfs.init() # Create dataset x,y = spiral_data(samples=100, classes=3) # Create Dense layer with 2 input features and 3 output values dense1 = Layer_Dense(2,3) # Create Relu Activation (to be used with Dense Layer) activation_relu = Activation_ReLU() # Create second Dense layer with 3 input features (as we take output of previous layer here) # and 3 output values dense2 = Layer_Dense(3, 3) # Create Softmax actiavtion (to be used with Dense layer): activation_softmax = Activation_Softmax() # Make a forward pass of our training data through this layer dense1.forward(x) # Make a forward pass through activation fucntion # it takes the output of first dense layer here activation_relu.forward(dense1.output)