10deb8f267e1d982415a23a8f5c8cc44da4f4671,tests/keras/layers/test_normalization.py,,test_batchnorm_mode_0_convnet,#,34

Before Change

    X = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
    model.fit(X, X, nb_epoch=5, verbose=0)
    norm_m0.input = K.variable(X)
    out = (norm_m0.get_output(train=True) - K.reshape(norm_m0.beta, (1, 3, 1, 1))) / K.reshape(norm_m0.gamma, (1, 3, 1, 1))

    assert_allclose(K.eval(K.mean(out, axis=(0, 2, 3))), 0.0, atol=1e-1)
    assert_allclose(K.eval(K.std(out, axis=(0, 2, 3))), 1.0, atol=1e-1)

After Change

    out = norm_m0.call(K.variable(X))
    out -= K.reshape(norm_m0.beta, (1, 3, 1, 1))
    out /= K.reshape(norm_m0.gamma, (1, 3, 1, 1))
    np_out = K.function([K.learning_phase()], [out])([1.])[0]

    assert_allclose(np.mean(np_out, axis=(0, 2, 3)), 0.0, atol=1e-1)
    assert_allclose(np.std(np_out, axis=(0, 2, 3)), 1.0, atol=1e-1)

Instances