def test_max_and_argmax_sparse(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0,0], [0,1], [1,1], [1,2], [1,2], [2,2], [2,2]], dtype=f32) s1 = np.array([[1,2], [2,3], [1,1], [2,0], [4,1], [3,3], [4,4]], dtype=f32) w = np.array([[1,2], [2,1], [1,2], [3,4], [5,6], [7,8], [9,13]], dtype=f32) m = np.array([[1,1], [1,1], [1,1], [1,1], [1,1], [1,1], [1,0]], dtype=f32) print("W:\n%r" % NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval()) init_out_max = T.zeros((n_time, n_batch), dtype=f32) init_out_arg = T.zeros((n_time, n_batch), dtype=f32) max1, arg1 = NativeOp.max_and_argmax_sparse(s0, s1, w, m, init_out_max, init_out_arg) W = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim) assert W.ndim == 3 max2, arg2 = T.max_and_argmax(W, axis=2) arg0 = np.array([[2, 2], [4, 1], [4, 3]]) max0 = np.array([[2, 2], [5, 2], [9, 8]]) arg1 = arg1.eval() arg2 = arg2.eval() max1 = max1.eval() max2 = max2.eval() print("arg0:\n%r" % arg0) print("arg1:\n%r" % arg1) print("arg2:\n%r" % arg2) print("max0:\n%r" % max0) print("max1:\n%r" % max1) print("max2:\n%r" % max2) assert_almost_equal(arg0, arg1) assert_almost_equal(arg0, arg2) assert_almost_equal(max0, max1) assert_almost_equal(max0, max2)
def test_crossentropy_softmax_and_gradient_z_sparse(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array( [[.3, 1], [.7, .4], [.1, .6], [.3, .2], [.6, .3], [.4, .5], [.6, 9]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) print("y_target:\n%r" % NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval()) np.random.seed(123) z = np.random.randn(n_time, n_batch, n_dim).astype(f32) print("z:\n%r" % z) print("y (softmax(z)):\n%r" % TheanoUtil.softmax(z).eval()) z_mask = np.array([[1, 1], [1, 1], [1, 1]], dtype=f32) args = (z, z_mask, s0, s1, w, m) ce1, gradz1 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(*args) ce2, gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse__slow( *args) ce1 = ce1.eval() ce2 = ce2.eval() gradz1 = gradz1.eval() gradz2 = gradz2.eval() print("ce1:\n%r" % ce1) print("ce2:\n%r" % ce2) print("gradz1:\n%r" % gradz1) print("gradz2:\n%r" % gradz2) assert_almost_equal(ce1, ce2) assert_almost_equal(gradz1, gradz2)
def test_crossentropy_softmax_and_gradient_z_sparse(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array([[.3,1], [.7,.4], [.1,.6], [.3,.2], [.6,.3], [.4,.5], [.6,9]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) print("y_target:\n%r" % NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval()) np.random.seed(123) z = np.random.randn(n_time, n_batch, n_dim).astype(f32) print("z:\n%r" % z) print("y (softmax(z)):\n%r" % TheanoUtil.softmax(z).eval()) z_mask = np.array([[1,1], [1,1], [1,1]], dtype=f32) args = (z, z_mask, s0, s1, w, m) ce1, gradz1 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(*args) ce2, gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse__slow(*args) ce1 = ce1.eval() ce2 = ce2.eval() gradz1 = gradz1.eval() gradz2 = gradz2.eval() print("ce1:\n%r" % ce1) print("ce2:\n%r" % ce2) print("gradz1:\n%r" % gradz1) print("gradz2:\n%r" % gradz2) assert_almost_equal(ce1, ce2) assert_almost_equal(gradz1, gradz2)
def test_max_and_argmax_sparse(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array([[1, 2], [2, 1], [1, 2], [3, 4], [5, 6], [7, 8], [9, 13]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) print("W:\n%r" % NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval()) init_out_max = T.zeros((n_time, n_batch), dtype=f32) init_out_arg = T.zeros((n_time, n_batch), dtype=f32) max1, arg1 = NativeOp.max_and_argmax_sparse(s0, s1, w, m, init_out_max, init_out_arg) W = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim) assert W.ndim == 3 max2, arg2 = T.max_and_argmax(W, axis=2) arg0 = np.array([[2, 2], [4, 1], [4, 3]]) max0 = np.array([[2, 2], [5, 2], [9, 8]]) arg1 = arg1.eval() arg2 = arg2.eval() max1 = max1.eval() max2 = max2.eval() print("arg0:\n%r" % arg0) print("arg1:\n%r" % arg1) print("arg2:\n%r" % arg2) print("max0:\n%r" % max0) print("max1:\n%r" % max1) print("max2:\n%r" % max2) assert_almost_equal(arg0, arg1) assert_almost_equal(arg0, arg2) assert_almost_equal(max0, max1) assert_almost_equal(max0, max2)
def test_sparse_to_dense(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0,0], [0,1], [1,1], [1,2], [1,2], [2,2], [2,2]], dtype=f32) s1 = np.array([[1,2], [2,3], [1,1], [2,0], [4,1], [3,3], [4,4]], dtype=f32) w = np.array([[1,2], [2,1], [1,2], [3,4], [5,6], [7,8], [9,9]], dtype=f32) m = np.array([[1,1], [1,1], [1,1], [1,1], [1,1], [1,1], [1,0]], dtype=f32) W = np.array( [[[0, 1, 2, 0, 0], [0, 0, 2, 0, 0]], [[0, 1, 3, 0, 5], [0, 2, 0, 1, 0]], [[0, 0, 0, 7, 9], [4, 6, 0, 8, 0]]], dtype=f32) assert W.shape == (n_time, n_batch, n_dim) W2 = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval() assert_almost_equal(W, W2)
def test_sparse_to_dense(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array([[1, 2], [2, 1], [1, 2], [3, 4], [5, 6], [7, 8], [9, 9]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) W = np.array([[[0, 1, 2, 0, 0], [0, 0, 2, 0, 0]], [[0, 1, 3, 0, 5], [0, 2, 0, 1, 0]], [[0, 0, 0, 7, 9], [4, 6, 0, 8, 0]]], dtype=f32) assert W.shape == (n_time, n_batch, n_dim) W2 = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval() assert_almost_equal(W, W2)
def test_sparse_to_dense_2(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array([[.3,1], [.7,.4], [.1,.6], [.3,.2], [.6,.3], [.4,.5], [.6,9]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) W = np.array( [[[0,.3,.7, 0, 0], [0, 0, 1, 0, 0]], [[0,.1,.3, 0,.6], [0,.6, 0,.4, 0]], [[0, 0, 0,.4,.6], [.2,.3,0,.5, 0]]], dtype=f32) assert W.shape == (n_time, n_batch, n_dim) W2 = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval() assert_almost_equal(W, W2) assert_almost_equal( np.sum(W, axis=2), np.ones((n_time, n_batch), dtype=f32) )
def test_sparse_to_dense_2(): n_time = 3 n_batch = 2 n_dim = 5 s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]], dtype=f32) s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]], dtype=f32) w = np.array( [[.3, 1], [.7, .4], [.1, .6], [.3, .2], [.6, .3], [.4, .5], [.6, 9]], dtype=f32) m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]], dtype=f32) W = np.array([[[0, .3, .7, 0, 0], [0, 0, 1, 0, 0]], [[0, .1, .3, 0, .6], [0, .6, 0, .4, 0]], [[0, 0, 0, .4, .6], [.2, .3, 0, .5, 0]]], dtype=f32) assert W.shape == (n_time, n_batch, n_dim) W2 = NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval() assert_almost_equal(W, W2) assert_almost_equal(np.sum(W, axis=2), np.ones((n_time, n_batch), dtype=f32))