def loadData():
mnist_dataset = mnist_data_loader.read_data_sets("./MNIST_data/",
one_hot=False)
# training dataset
train_set = mnist_dataset.train
# test dataset
test_set = mnist_dataset.test
print("Training dataset size: ", train_set.num_examples)
print("Test dataset size: ", test_set.num_examples)
return train_set, test_set
def dataload(self):
# Data Preprocessing
mnist_dataset = mnist_data_loader.read_data_sets("./MNIST_data/")
train_set = mnist_dataset.train
test_set = mnist_dataset.test
# train dataset
train_set = train_set.next_batch(self.batch_size)
self.input, self.label = train_set
# test dataset
test_set = test_set.next_batch(1000)
self.test_input, self.test_label = test_set
plt.savefig("number_a.png", bbox_inches='tight')
plt.show()
plt.imshow(np.reshape(img_map_six, [28, 28]), cmap='gray')
plt.savefig("number_b.png", bbox_inches='tight')
plt.show()
def normalize(y):
y_norm = (y - np.min(y)) / (np.max(y) - np.min(y))
return y_norm.astype(int)
if __name__ == "__main__":
mnist_dataset = mnist_data_loader.read_data_sets("./MNIST_data/",
one_hot=False)
# training dataset
train_set = mnist_dataset.train
# train_set.labels = normalize(train_set.labels)
# test dataset
test_set = mnist_dataset.test
# test_set.labels = normalize(test_set.labels)
print("Training dataset size: ", train_set.num_examples)
print("Test dataset size: ", test_set.num_examples)
batch_size = 200
max_epoch = 100
reg = 1e-5
loss_history = []
acc_history = []