adding io_utils test for hdf5matrix (keras-team#6610)

* adding io_utils test for hdf5matrix

* incorporating pep8 and feedback from @fchollet

tests/keras/utils/io_utils_test.py

@@ -0,0 +1,65 @@
+'''Tests for functions in io_utils.py.
+'''
+import os
+import pytest
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils.io_utils import HDF5Matrix
+import numpy as np
+import warnings
+import h5py
+
+
+def create_dataset(h5_path='test.h5'):
+    X = np.random.randn(200, 10).astype('float32')
+    y = np.random.randint(0, 2, size=(200, 1))
+    f = h5py.File(h5_path, 'w')
+    # Creating dataset to store features
+    X_dset = f.create_dataset('my_data', (200, 10), dtype='f')
+    X_dset[:] = X
+    # Creating dataset to store labels
+    y_dset = f.create_dataset('my_labels', (200, 1), dtype='i')
+    y_dset[:] = y
+    f.close()
+
+
+def test_io_utils():
+    '''Tests the HDF5Matrix code using the sample from @jfsantos at
+    https://gist.github.com/jfsantos/e2ef822c744357a4ed16ec0c885100a3
+    '''
+    h5_path = 'test.h5'
+    create_dataset(h5_path)
+
+    # Instantiating HDF5Matrix for the training set, which is a slice of the first 150 elements
+    X_train = HDF5Matrix(h5_path, 'my_data', start=0, end=150)
+    y_train = HDF5Matrix(h5_path, 'my_labels', start=0, end=150)
+
+    # Likewise for the test set
+    X_test = HDF5Matrix(h5_path, 'my_data', start=150, end=200)
+    y_test = HDF5Matrix(h5_path, 'my_labels', start=150, end=200)
+
+    # HDF5Matrix behave more or less like Numpy matrices with regards to indexing
+    assert y_train.shape == (150, 1), 'HDF5Matrix shape should match input array'
+    # But they do not support negative indices, so don't try print(X_train[-1])
+
+    model = Sequential()
+    model.add(Dense(64, input_shape=(10,), activation='relu'))
+    model.add(Dense(1, activation='sigmoid'))
+
+    model.compile(loss='binary_crossentropy', optimizer='sgd')
+
+    # Note: you have to use shuffle='batch' or False with HDF5Matrix
+    model.fit(X_train, y_train, batch_size=32, shuffle='batch', verbose=False)
+    # test that evalutation and prediction don't crash and return reasonable results
+    out_pred = model.predict(X_test, batch_size=32, verbose=False)
+    out_eval = model.evaluate(X_test, y_test, batch_size=32, verbose=False)
+
+    assert out_pred.shape == (50, 1), 'Prediction shape does not match'
+    assert out_eval.shape == (), 'Shape of evaluation does not match'
+    assert out_eval > 0, 'Evaluation value does not meet criteria: {}'.format(out_eval)
+
+    os.remove(h5_path)
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])