Modelo de Convulocionales con Keras y Python, Ejercicios de Sistemas Operativos

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Alumno: Huancahuire Velasquez Eddie from keras.datasets import cifar10 from matplotlib import pyplot from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras.constraints import maxnorm $t cargar datos (X_train, y_train), (X_test, y_test) = cifar10.load_data() Downloading data from https://www.cs.toronto.edu/-kriz/cifar-10-python.tar.gz 170500096/170498071 [= ] - 3s 0us/step 170508288/170498071 [= ] - 3s Ous/step num_pixeles = X_train.shape[1]*X_train.shape[2] X_train = X_train.reshape((X_train.shape[0],32,32,3)).astype('float32') X_test = X_test.reshape((X_test.shape[0],32,32,3)).astype('float32') X_train = X_train/255 X_test = X_test/255 print(y_train[0]) print(y_test[0]) [6] [3] y_train = np_utils.to_categorical(y_train) y_test = np_utils.to_categorical(y_test) print(y_train[0]) print(y_test[0]) [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 6. 6. 0. 0. 0. 0.] num_clases = y_train.shape[1] num_clases