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)