Python tensorflow.python.keras.datasets.mnist.load_data() Examples

The following are 6 code examples of tensorflow.python.keras.datasets.mnist.load_data(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.keras.datasets.mnist , or try the search function .
Example #1
Source File: mnist.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def _make_dataset(self):
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)

    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.repeat()
    dataset = dataset.shuffle(self.batch_size * 3)
    dataset = dataset.batch(self.batch_size)
    def _map_fn(image, label):
      image = tf.to_float(image) / 255.
      label.set_shape([self.batch_size])
      label = tf.cast(label, dtype=tf.int32)
      label_onehot = tf.one_hot(label, 10)
      image = tf.reshape(image, [self.batch_size, 28, 28, 1])
      return common.ImageLabelOnehot(
          image=image, label=label, label_onehot=label_onehot)

    self.dataset = dataset.map(_map_fn)
Example #2
Source File: mnist.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def _make_dataset(self):
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)

    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.repeat()
    dataset = dataset.shuffle(self.batch_size * 3)
    dataset = dataset.batch(self.batch_size)
    def _map_fn(image, label):
      image = tf.to_float(image) / 255.
      label.set_shape([self.batch_size])
      label = tf.cast(label, dtype=tf.int32)
      label_onehot = tf.one_hot(label, 10)
      image = tf.reshape(image, [self.batch_size, 28, 28, 1])
      return common.ImageLabelOnehot(
          image=image, label=label, label_onehot=label_onehot)

    self.dataset = dataset.map(_map_fn)
Example #3
Source File: mnist.py    From models with Apache License 2.0 6 votes vote down vote up 
def _make_dataset(self):
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)

    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.repeat()
    dataset = dataset.shuffle(self.batch_size * 3)
    dataset = dataset.batch(self.batch_size)
    def _map_fn(image, label):
      image = tf.to_float(image) / 255.
      label.set_shape([self.batch_size])
      label = tf.cast(label, dtype=tf.int32)
      label_onehot = tf.one_hot(label, 10)
      image = tf.reshape(image, [self.batch_size, 28, 28, 1])
      return common.ImageLabelOnehot(
          image=image, label=label, label_onehot=label_onehot)

    self.dataset = dataset.map(_map_fn)
Example #4
Source File: mnist.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def _make_dataset(self):
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)

    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.repeat()
    dataset = dataset.shuffle(self.batch_size * 3)
    dataset = dataset.batch(self.batch_size)
    def _map_fn(image, label):
      image = tf.to_float(image) / 255.
      label.set_shape([self.batch_size])
      label = tf.cast(label, dtype=tf.int32)
      label_onehot = tf.one_hot(label, 10)
      image = tf.reshape(image, [self.batch_size, 28, 28, 1])
      return common.ImageLabelOnehot(
          image=image, label=label, label_onehot=label_onehot)

    self.dataset = dataset.map(_map_fn)
Example #5
Source File: io_api_test.py    From autokeras with MIT License 5 votes vote down vote up 
def test_io_api(tmp_path):
    num_instances = 100
    (image_x, train_y), (test_x, test_y) = mnist.load_data()
    (text_x, train_y), (test_x, test_y) = utils.imdb_raw(
        num_instances=num_instances)

    image_x = image_x[:num_instances]
    text_x = text_x[:num_instances]
    structured_data_x = utils.generate_structured_data(num_instances=num_instances)
    classification_y = utils.generate_one_hot_labels(num_instances=num_instances,
                                                     num_classes=3)
    regression_y = utils.generate_data(num_instances=num_instances, shape=(1,))

    # Build model and train.
    automodel = ak.AutoModel(
        inputs=[
            ak.ImageInput(),
            ak.TextInput(),
            ak.StructuredDataInput()
        ],
        outputs=[ak.RegressionHead(metrics=['mae']),
                 ak.ClassificationHead(loss='categorical_crossentropy',
                                       metrics=['accuracy'])],
        directory=tmp_path,
        max_trials=2,
        tuner=ak.RandomSearch,
        seed=utils.SEED)
    automodel.fit([
        image_x,
        text_x,
        structured_data_x
    ],
        [regression_y, classification_y],
        epochs=1,
        validation_split=0.2)
Example #6
Source File: utils.py    From TF.Keras-Commonly-used-models with Apache License 2.0 5 votes vote down vote up 
def get_mnist_dataset():
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = X_train.astype('float32') / 255
    X_test = X_test.astype('float32') / 255
    X_train = X_train[..., None]
    X_test = X_test[..., None]
    Y_train = keras.utils.to_categorical(y_train, 10)
    Y_test = keras.utils.to_categorical(y_test, 10)

    return (X_train, Y_train), (X_test, Y_test)