Create a Program to Implement Cross Entropy in Python Assignment Solution

Instructions

Write a program to implement cross entropy in python.

Requirements and Specifications

Source Code

import numpy as np from keras.datasets import mnist from keras.utils import np_utils from dense import Dense from softmax import Softmax from convolutional import Convolutional from reshape import Reshape from activations import Tanh, Sigmoid from losses import binary_cross_entropy, binary_cross_entropy_prime def preprocess_data(x, y, limit): ''' Will limit our data since using the whole thing will take forever on a cpu especially since we're implementing this from scratch. ''' x = x.reshape(len(x), 1, 28, 28) x = x.astype("float32") / 255 y = np_utils.to_categorical(y) y = y.reshape(len(y), 10, 1) return x[:limit], y[:limit] # load MNIST from server, limit to 100 images per class since we're not training on GPU (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, y_train = preprocess_data(x_train, y_train, 100) x_test, y_test = preprocess_data(x_test, y_test, 100) # TODO: Add our layers and the flow of input into this list. network = [ Convolutional((1, 28, 28), 3, 5), Sigmoid(), Reshape((5, 26, 26), (5 * 26 * 26, 1)), Dense(5 * 26 * 26, 100), Sigmoid(), Dense(100, 100), Sigmoid(), Dense(100, 10), Softmax() ] epochs = 100 learning_rate = 0.1 #train for e in range(epochs): error = 0 for x, y in zip(x_train, y_train): # forward output = x for layer in network: output = layer.forward(output) # TODO: update our error error += binary_cross_entropy(y,output) grad = binary_cross_entropy_prime(y,output) # TODO: perform back prop for layer in reversed(network): grad = layer.backward(grad,learning_rate) error /= len(x_train) print(f"{e + 1}/{epochs}, error={error}") # TODO: run the test data through and print out your predictions for x, y in zip(x_test, y_test): output = x for layer in network: output = layer.forward(output) print(f"y_pred: {np.argmax(output)}, y_true: {np.argmax(y)}")