def f1():
# calculate backward combine softmax with loss
softmax_loss = Activation_Softmax_Loss_CategoricalCorssentropy()
softmax_loss.backward(softmax_output, class_targets)
dvalues1 = softmax_loss.dinputs
# 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) # Perform a forward pass through second Dense layer takes # output of activation function of first layer as inputs dense2.forward(activation1_relu.output) # Perform a forward pass through the activation/loss function # take the output of second dense layer here and return loss loss = loss_activation.forward(dense2.output, y)
import nnfs
from libraries import Activation_Softmax_Loss_CategoricalCorssentropy
from libraries import Activation_Softmax
from libraries import Loss_CategoricalCrossentropy
from timeit import timeit
# initialize nnfs dataset
nnfs.init()
# dummy output from activation softmax
softmax_output = np.array([[0.7, 0.1, 0.2], [0.1, 0.5, 0.4], [0.02, 0.9,
0.08]])
class_targets = np.array([0, 1, 1])
softmax_loss = Activation_Softmax_Loss_CategoricalCorssentropy()
softmax_loss.backward(softmax_output, class_targets)
dvalues1 = softmax_loss.dinputs
activation = Activation_Softmax()
activation.output = softmax_output
loss = Loss_CategoricalCrossentropy()
loss.backward(softmax_output, class_targets)
# update activation softmax backward
activation.backward(loss.dinputs)
dvalues2 = activation.dinputs
print('Gradient: combined loss and activation:')