from inti_param import InitParam from optimization import Optimization import numpy as np from image_prediction import image_predict import matplotlib.pyplot as plt learning_rate = 0.005 from cat_noncat import CatNonCat X, Y, classes, num_px = CatNonCat.load_data() w, b = InitParam.initialize_params(X.shape[0]) params, grads, cost = Optimization.optimize(w, b, X, Y, 2000, learning_rate, False) image_predict(params, classes, num_px) costs = np.squeeze(cost) plt.plot(costs) plt.ylabel('cost') plt.xlabel('iterations (per hundreds)') plt.title("Learning rate =" + str(learning_rate)) # y_predict = predict(params['w'], params['b'], X) # print("train accuracy: {} %".format(100 - np.mean(np.abs(y_predict - Y)) * 100)) # #Common Model Algorithms # from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process # #Common Model Helpers # from sklearn.preprocessing import OneHotEncoder, LabelEncoder # from sklearn import feature_selection # from sklearn import model_selection # from sklearn import metrics
import pandas as pd # import matplotlib.pyplot as plt from prediction import predict learning_rate = 0.005 TRAIN_DATA = '\\input\\train\\' TEST_DATA = '\\input\\test\\' FILE_COUNT = 10000 # Train on dataset print('Training..........') X_train, Y_train = load_cat_vs_dog_data(TRAIN_DATA, FILE_COUNT, shuffle=True) # X_train = X_train.T # Y_train = Y_train.T w, b = InitParam.initialize_params(X_train.shape[0]) params, grads, cost = Optimization.optimize(w, b, X_train, Y_train, 500, learning_rate, False) FILE_COUNT = 12500 print('Predicting..........') X_test, id_list = load_cat_vs_dog_test_data(TEST_DATA, FILE_COUNT) Y_Predict = predict(params['w'], params['b'], X_test) my_solution = pd.DataFrame(Y_Predict.T, id_list, columns=["Id, Label"]) my_solution.to_csv("my_solution_one.csv", index_label=["Id"]) # print(my_solution) # #Common Model Algorithms # from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process # #Common Model Helpers # from sklearn.preprocessing import OneHotEncoder, LabelEncoder # from sklearn import feature_selection