def test_mnist_tutorial_uncompressed():
"""test_mnist_tutorial_uncompressed"""
image_filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"test_mnist",
"t10k-images-idx3-ubyte")
label_filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"test_mnist",
"t10k-labels-idx1-ubyte")
d_train = mnist_io.MNISTDataset(
image_filename,
label_filename,
batch=1)
d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y))
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(d_train, epochs=5)
def test_mnist_dataset(self):
"""Test case for MNIST Dataset.
"""
mnist_filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "test_mnist",
"mnist.npz")
with np.load(mnist_filename) as f:
(x_test, y_test) = f['x_test'], f['y_test']
image_filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "test_mnist",
"t10k-images-idx3-ubyte.gz")
label_filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "test_mnist",
"t10k-labels-idx1-ubyte.gz")
image_dataset = mnist_io.MNISTImageDataset(image_filename, batch=3)
label_dataset = mnist_io.MNISTLabelDataset(label_filename, batch=3)
dataset = mnist_io.MNISTDataset(image_filename, label_filename)
iterator = data.Dataset.zip(
(image_dataset, label_dataset)).make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
l = len(y_test)
for i in range(0, l - 1, 3):
v_x = x_test[i:i + 3]
v_y = y_test[i:i + 3]
m_x, m_y = sess.run(get_next)
self.assertAllEqual(v_y, m_y)
self.assertAllEqual(v_x, m_x)
v_x = x_test[l - 1:l]
v_y = y_test[l - 1:l]
m_x, m_y = sess.run(get_next)
self.assertAllEqual(v_y, m_y)
self.assertAllEqual(v_x, m_x)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
l = len(y_test)
for i in range(l):
v_x = x_test[i]
v_y = y_test[i]
m_x, m_y = sess.run(get_next)
self.assertAllEqual(v_y, m_y)
self.assertAllEqual(v_x, m_x)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def test_mnist_dataset():
"""Test case for MNIST Dataset.
"""
mnist_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_mnist", "mnist.npz")
with np.load(mnist_filename) as f:
(x_test, y_test) = f['x_test'], f['y_test']
image_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_mnist", "t10k-images-idx3-ubyte.gz")
label_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_mnist", "t10k-labels-idx1-ubyte.gz")
image_dataset = mnist_io.MNISTImageDataset(image_filename)
label_dataset = mnist_io.MNISTLabelDataset(label_filename)
i = 0
for m_x in image_dataset:
v_x = x_test[i]
assert np.alltrue(v_x == m_x.numpy())
i += 1
assert i == len(y_test)
i = 0
for m_y in label_dataset:
v_y = y_test[i]
assert np.alltrue(v_y == m_y.numpy())
i += 1
assert i == len(y_test)
dataset = mnist_io.MNISTDataset(image_filename, label_filename)
i = 0
for (m_x, m_y) in dataset:
v_x = x_test[i]
v_y = y_test[i]
assert np.alltrue(v_y == m_y.numpy())
assert np.alltrue(v_x == m_x.numpy())
i += 1
assert i == len(y_test)
