AI2020_Project/numbering.py

from sklearn.datasets import load_digits
import pylab as pl
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import os.path
import csv
import random

layers_dims=[64,60,10,10]



        
def predict_L_layer(X,parameters):
    AL,caches=L_model_forward(X,parameters)

    prediction=np.argmax(AL,axis=0)

    return prediction.reshape(1,prediction.shape[0])


def initialize_parameters_deep(layer_dims):
    np.random.seed(3)
    parameters = {}
    L = len(layer_dims) 
    for l in range(1, L):
        parameters['W' + str(l)] = np.random.randn(layer_dims[l],layer_dims[l-1])*0.01
        parameters['b' + str(l)] = np.zeros((layer_dims[l],1))

    return parameters


def linear_forward(A, W, b):
    Z = np.dot(W,A)+b

    cache = (A, W, b)
    
    return Z, cache

def sigmoid_(Z):
    return 1/(1+np.exp(-Z))

def relu_(Z):
    return Z*(Z>0)

def drelu_(Z):
    return 1. *(Z>0)

def dsigmoid_(Z):
    return sigmoid_(Z)*(1-sigmoid_(Z))

def sigmoid(Z):
    return sigmoid_(Z),Z

def relu(Z):
    return relu_(Z),Z

def linear_activation_forward(A_prev,W,b,activation):
    if activation == "sigmoid":
        Z, linear_cache = linear_forward(A_prev,W,b)
        A, activation_cache = sigmoid(Z)
        
    elif activation == "relu":
        Z, linear_cache = linear_forward(A_prev,W,b)
        A, activation_cache = relu(Z)
        
    cache = (linear_cache, activation_cache)
    
    return A, cache

def L_model_forward(X, parameters):
    caches = []
    A = X
    L = len(parameters) // 2   
    for l in range(1, L):
        A_prev = A 
        A, cache = linear_activation_forward(A_prev,parameters['W'+str(l)],parameters['b'+str(l)],"relu")
        caches.append(cache)
    AL, cache = linear_activation_forward(A,parameters['W'+str(L)],parameters['b'+str(L)],"sigmoid")
    caches.append(cache)

    return AL, caches

def compute_cost(AL, Y):
    m=Y.shape[1]
    cost = -(1/m)*np.sum((Y*np.log(AL)+(1-Y)*np.log(1-AL)))
    cost=np.squeeze(cost)
    assert(cost.shape == ())
    return cost

def linear_backward(dZ, cache):
    A_prev, W, b = cache
    m = A_prev.shape[1]
    dW = (1/m)*np.dot(dZ,A_prev.T)
    db = (1/m)*np.sum(dZ,axis=1,keepdims=True)
    dA_prev = np.dot(W.T,dZ)
    
    return dA_prev, dW, db

def relu_backward(dA,activation_cache):
    return dA* drelu_(activation_cache)

def sigmoid_backward(dA,activation_cache):
    return dA* dsigmoid_(activation_cache)

def linear_activation_backward(dA, cache, activation):
    linear_cache, activation_cache = cache
    if activation == "relu":
        dZ = relu_backward(dA,activation_cache)
        dA_prev, dW, db = linear_backward(dZ,linear_cache)
    
    elif activation == "sigmoid":
        dZ = sigmoid_backward(dA,activation_cache)
        dA_prev, dW, db = linear_backward(dZ,linear_cache)
    return dA_prev,dW,db

def L_model_backward(AL, Y, caches):
    grads = {}
    L = len(caches)
    m = AL.shape[1]
    #Y = Y.reshape(AL.shape)
    
    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
    
    current_cache = caches[L-1]
    grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] = linear_activation_backward(dAL,current_cache,"sigmoid")
    
    for l in reversed(range(L-1)):
        current_cache = caches[l]
        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA"+str(l+1)],current_cache,"relu")
        grads["dA" + str(l)] = dA_prev_temp
        grads["dW" + str(l + 1)] = dW_temp
        grads["db" + str(l + 1)] = db_temp
    return grads

def update_parameters(parameters, grads, learning_rate):
    L = len(parameters) // 2 
    for l in range(L):
        parameters["W" + str(l+1)] = parameters["W" + str(l+1)]-(learning_rate)*grads["dW"+str(l+1)] 
        parameters["b" + str(l+1)] = parameters["b" + str(l+1)]-(learning_rate)*grads["db"+str(l+1)]
    return parameters



def L_layer_model(X, Y, layers_dims, learning_rate = 0.005, num_iterations = 3000, print_cost=False):
    np.random.seed(1)
    costs = [] 
    
    parameters = initialize_parameters_deep(layers_dims)
    
    for i in range(0, num_iterations):
        AL, caches = L_model_forward(X, parameters)
        cost = compute_cost(AL, Y)
        grads = L_model_backward(AL, Y, caches)
        parameters = update_parameters(parameters, grads, learning_rate)
        if print_cost and i % 1000 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
        if print_cost and i % 1000 == 0:
            costs.append(cost)
    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters


def num(size,target):
        numbers=[]
        imagesx=[]
        imagesy=[]

        while(len(numbers)!=size):
                x=random.randint(0,np.shape(target)[0]-1)
                y=random.randint(0,np.shape(target)[0]-1)

                num=str(target[x][1])+str(target[y][1])
                
                if(num not in numbers):
                        numbers.append(num)
                        imagesx.append(target[x][0])
                        imagesy.append(target[y][0])
        return(numbers,imagesx,imagesy)

def pred(xnum,ynum):
        
    xnum=np.array(xnum)
    ynum=np.array(ynum)
    #pl.gray()
    #pl.matshow(xnum[0])
    #pl.show()

    imgx=xnum.reshape((64,1)).T
    imgx = sc.transform(imgx)
    imgx=imgx.T

    imgy=ynum.reshape((64,1)).T
    imgy = sc.transform(imgy)
    imgy=imgy.T

    predicted_digitx=predict_L_layer(imgx,parameters)
    predicted_digity=predict_L_layer(imgy,parameters)
    return int(str(predicted_digitx[0][0]) + str(predicted_digity[0][0]))



digits=load_digits()
images_and_labels=list(zip(digits.images,digits.target))
print()



n_samples=len(digits.images)


x=digits.images.reshape((n_samples,-1))

y=digits.target





X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)
np.save('Ytest.npy',y_test )
np.save('Xtest.npy',X_test )

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

X_train=X_train.T
X_test=X_test.T
y_train=y_train.reshape(y_train.shape[0],1)
y_test=y_test.reshape(y_test.shape[0],1)
y_train=y_train.T
y_test=y_test.T



Y_train_=np.zeros((10,y_train.shape[1]))
for i in range(y_train.shape[1]):
    Y_train_[y_train[0,i],i]=1
Y_test_=np.zeros((10,y_test.shape[1]))
for i in range(y_test.shape[1]):
    Y_test_[y_test[0,i],i]=1


if (os.path.isfile("NN.npy")):
    
    parameters=np.load('NN.npy',allow_pickle='TRUE').item()
else:
    parameters = L_layer_model(X_train, Y_train_, layers_dims, num_iterations = 50000, print_cost = True)
    np.save('NN.npy', parameters)