New H5 version of MNIST

data/mnist.py

@@ -0,0 +1,30 @@
+import h5py
+import numpy as np
+from keras.datasets import mnist
+from keras.utils import to_categorical
+
+# input image dimensions
+img_rows, img_cols = 28, 28
+# the data, shuffled and split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+input_shape = (img_rows, img_cols, 1)
+x_train = x_train.astype('float16')
+x_test = x_test.astype('float16')
+inputs = np.concatenate((x_train,x_test)) / 255
+labels = np.concatenate((y_train,y_test)) # ints, 0 to 10
+###########################################
+# fix mis-labeled image(s) in Keras dataset
+labels[10994] = 9
+###########################################
+targets = to_categorical(labels).astype("uint8")
+string = h5py.special_dtype(vlen=str)
+labels = np.array([str(label) for label in labels], dtype=string)
+
+print("creating h5...")
+with h5py.File("mnist.h5", "w") as h5:
+    dset = h5.create_dataset('inputs', data=[inputs], compression='gzip', compression_opts=9)
+    dset = h5.create_dataset('targets', data=[targets], compression='gzip', compression_opts=9)
+    dset = h5.create_dataset('labels', data=[labels], compression='gzip', compression_opts=9)
+print("done!")