mnist_dnn_spikey.py File Reference

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Namespaces
	mnist_dnn_spikey

Variables
int	mnist_dnn_spikey.batch_size = 128
int	mnist_dnn_spikey.num_classes = 10
int	mnist_dnn_spikey.epochs = 25
	mnist_dnn_spikey.x_test = x_test.reshape(10000, 28,28,1)
	mnist_dnn_spikey.y_test = keras.utils.to_categorical(y_test, num_classes)
	mnist_dnn_spikey.x_train = x_train.reshape(60000,28,28,1)
	mnist_dnn_spikey.y_train = keras.utils.to_categorical(y_train, num_classes)
	mnist_dnn_spikey.x_train_new = x_train
	new_im = np.zeros((14,14)) for i in range(0,28,2): for j in range(0,28,2): x_train_new[counter][int(i/2)][int(j/2)] = (image[i][j] + image[i+1][j] + image[i][j+1] + image[i+1][j+1])/4.0 x_train_new.append(new_im) More...
	mnist_dnn_spikey.x_test_new = x_test
	mnist_dnn_spikey.model = Sequential()
	mnist_dnn_spikey.loss
	mnist_dnn_spikey.optimizer
	mnist_dnn_spikey.metrics
	mnist_dnn_spikey.history
	mnist_dnn_spikey.score = model.evaluate(x_test_new, y_test, verbose=0)
	mnist_dnn_spikey.json_string = model.to_json()