tensorflow-io

import tensorflow as tf import tensorflow_io as tfio # Read the MNIST data into an IODataset directly from URLs dataset_url = "https://storage.googleapis.com/cvdf-datasets/mnist/" d_train = tfio.IODataset.from_mnist( dataset_url + "train-images-idx3-ubyte.gz", dataset_url + "train-labels-idx1-ubyte.gz", ) # Shuffle the elements of the dataset. d_train = d_train.shuffle(buffer_size=1024) # By default image data is uint8, so convert to float32 using map(). d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y)) # Prepare batches the data just like any other tf.data.Dataset d_train = d_train.batch(32) # Build a simple Keras model 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), ]) # Compile and fit the model (example uses a small number of steps for brevity) model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) print("Fitting model...") # Use a small number of steps per epoch for quick demonstration model.fit(d_train, epochs=1, steps_per_epoch=2) print("Model fitting complete.")