def test_fwd_pass_connections_and_gradient(self):
batch_size = 64
width = 14
height = 28
hyper = {
'width_height': (width, height, 1),
'model_type': 'GS',
'batch_size': batch_size,
'temp': tf.constant(0.1)
}
shape = (batch_size, width, height, 1)
x_upper, x_lower = create_upper_and_lower(shape=shape)
sop = SOP(hyper=hyper)
with tf.GradientTape() as tape:
logits = sop.call(x_upper=x_upper)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=x_lower,
logits=logits)
grad = tape.gradient(sources=sop.trainable_variables, target=loss)
self.assertTrue(grad is not None)
def test_fwd_pass_connections_and_gradient():
batch_size, width, height, rgb, sample_size = 64, 14, 28, 1, 1
hyper = {
'width_height': (width, height, 1),
'model_type': 'GS',
'batch_size': batch_size,
'units_per_layer': 240,
'temp': tf.constant(0.1)
}
shape = (batch_size, width, height, rgb)
x_upper, x_lower = create_upper_and_lower_dummy_data(shape=shape)
sop = SOP(hyper=hyper)
with tf.GradientTape() as tape:
logits = sop.call(x_upper=x_upper)
x_lower = tf.reshape(x_lower, x_lower.shape + (sample_size, ))
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=x_lower,
logits=logits)
grad = tape.gradient(sources=sop.trainable_variables, target=loss)
print('\nTEST: Forward pass and gradient computation')
assert grad is not None