def test_softmax_ce_with_logits(self):
my_graph = ad.SoftmaxCEWithLogits(labels=self.my_w3, logits=self.my_w4)
tf_graph = tf.nn.softmax_cross_entropy_with_logits(labels=self.tf_w3,
logits=self.tf_w4)
wrt_vars = [self.my_w4]
tf_vars = [self.tf_w4]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def custom_einsum_f(self, op_str, my_args, my_wrt, tf_args=None, tf_wrt=None):
my_graph = ad.Einsum(op_str, *my_args)
if tf_args and tf_wrt is not None:
tf_graph = tf.einsum(op_str, *tf_args)
utils.custom_test(self, my_graph, my_wrt, tf_graph, tf_wrt)
else:
utils.custom_test(self, my_graph, my_wrt)
def test_softmax0(self):
# is this a correct test? Should the gradients always be this small?
my_graph = ad.Softmax(self.my_w4)
tf_graph = tf.nn.softmax(self.tf_w4)
wrt_vars = [self.my_w4]
tf_vars = [self.tf_w4]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_reshape(self):
shp = (1, 15)
my_graph = ad.Reshape(self.my_w1, shp)
tf_graph = tf.reshape(self.tf_w1, shp)
wrt_vars = [self.my_w1]
tf_vars = [self.tf_w1]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_pad(self):
pad_val = [[1, 0], [2, 2]]
constant_values = [0, 0]
my_graph = ad.Pad(self.my_w0, pad_val, constant_values=constant_values)
tf_graph = tf.pad(self.tf_w0,
pad_val,
constant_values=constant_values[0])
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_negate(self):
my_graph = ad.Negate(self.my_w0)
tf_graph = tf.negative(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_normal_distribution(self):
my_graph = ad.NormalDistribution(self.my_w5, 0, 1)
wrt_vars = [self.my_w5]
utils.custom_test(self, my_graph, wrt_vars)
def test_frobenius_norm(self):
my_graph = ad.FrobeniusNorm(self.my_w3)
tf_graph = tf.norm(self.tf_w3)
wrt_vars = [self.my_w3]
tf_vars = [self.tf_w3]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_concat2(self):
my_graph = ad.Concat(self.my_w2, self.my_w3, axis=1)
tf_graph = tf.concat([self.tf_w2, self.tf_w3], axis=1)
wrt_vars = [self.my_w2, self.my_w3]
tf_vars = [self.tf_w2, self.tf_w3]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_concat1(self):
my_graph = ad.Concat(self.my_w0, self.my_w2, axis=0)
tf_graph = tf.concat([self.tf_w0, self.tf_w2], axis=0)
wrt_vars = [self.my_w0, self.my_w2]
tf_vars = [self.tf_w0, self.tf_w2]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_reducesumkeepdims1(self):
axes = [0, 2]
my_graph = ad.ReduceSumKeepDims(self.my_w3, axes=axes)
wrt_vars = [self.my_w3]
utils.custom_test(self, my_graph, wrt_vars)
def test_all(self):
my_vars = [self.my_x, self.my_w1, self.my_w2]
tf_vars = [self.tf_x, self.tf_w1, self.tf_w2]
utils.custom_test(self, self.my_graph, my_vars, self.tf_graph, tf_vars)
def test_recipr(self):
my_graph = ad.Recipr(self.my_w0)
tf_graph = tf.reciprocal(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_relu(self):
my_graph = ad.ReLU(self.my_w0)
tf_graph = tf.nn.relu(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_sigmoid(self):
my_graph = ad.Sigmoid(self.my_w0)
tf_graph = tf.nn.sigmoid(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_slice5(self):
my_graph = self.my_w1[::-2]
tf_graph = self.tf_w1[::-2]
wrt_vars = [self.my_w1]
tf_vars = [self.tf_w1]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_slice3(self):
my_graph = self.my_w1[2, 2:4]
tf_graph = self.tf_w1[2, 2:4]
wrt_vars = [self.my_w1]
tf_vars = [self.tf_w1]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def custom_test(self, my_graph, tf_graph, my_vars, tf_vars):
utils.custom_test(self, my_graph, my_vars, tf_graph, tf_vars)
def test_softmax2(self):
my_graph = ad.Softmax(self.my_w6)
tf_graph = tf.nn.softmax(self.tf_w6)
wrt_vars = [self.my_w6]
tf_vars = [self.tf_w6]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_identity(self):
my_graph = ad.Identity(self.my_w0)
tf_graph = tf.identity(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_all(self):
my_vars = [self.my_x, self.my_w, self.my_b, self.var_mul]
tf_vars = [self.tf_x, self.tf_w, self.tf_b, self.tf_mul]
utils.custom_test(self, self.my_graph, my_vars, self.tf_graph, tf_vars)
def test_tanh(self):
my_graph = ad.Tanh(self.my_w0)
tf_graph = tf.tanh(self.tf_w0)
wrt_vars = [self.my_w0]
tf_vars = [self.tf_w0]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_mul(self):
my_graph = self.my_h * self.my_b0 * self.my_b1 + self.my_b2
tf_graph = self.tf_h * self.tf_b0 * self.tf_b1 + self.tf_b2
wrt_vars = [self.my_b0, self.my_b1, self.my_b2]
tf_vars = [self.tf_b0, self.tf_b1, self.tf_b2]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
def test_matmul(self):
my_graph = ad.MatMul(self.my_w0, self.my_w1)
tf_graph = tf.matmul(self.tf_w0, self.tf_w1)
wrt_vars = [self.my_w0, self.my_w1]
tf_vars = [self.tf_w0, self.tf_w1]
utils.custom_test(self, my_graph, wrt_vars, tf_graph, tf_vars)
