def test_train_custom_builder2(self):
"""
Build a custom Regression model whose Model graph has the rhombic design
"""
print("Test 1: Rhombic Design\n")
# Define the builder
builder = StaticBuilder('CustBuild')
enc_dirs = {
'numlayers': 2,
'numnodes': 128,
'activations': 'leaky_relu',
'netgrowrate': 1.0
}
input_dim = 2
in0 = builder.addInput(input_dim, name="Features")
enc1 = builder.addTransformInner(10,
main_inputs=in0,
directives=enc_dirs)
enc2 = builder.addTransformInner(10,
main_inputs=in0,
directives=enc_dirs)
builder.addTransformInner(1,
main_inputs=[enc1, enc2],
numlayers=1,
activations='linear',
name='Prediction')
reg = Regression(input_dim=2, output_dim=1, builder=builder)
# save_on_valid_improvement=True) # OK!
reg.train(dataset, num_epochs=10)
def test_train_custom_builder(self):
"""
"""
print("Test 0: Chain of Encoders\n")
# Define a builder. Get more control on the directives of each node
enc_dirs = {
'numlayers': 2,
'numnodes': 128,
'activations': 'leaky_relu',
'netgrowrate': 1.0
}
input_dim, output_dim = 2, 1
builder = StaticBuilder('CustBuild')
in0 = builder.addInput(input_dim, name="Features")
enc1 = builder.addTransformInner(5, main_inputs=in0, **enc_dirs)
builder.addTransformInner(state_size=1,
main_inputs=enc1,
directives=enc_dirs,
name='Prediction')
# Pass the builder object to the Regression Model
reg = Regression(builder=builder, output_dim=output_dim)
# save_on_valid_improvement=True) # ok!
reg.train(dataset, num_epochs=10)
def test_BuildModel3(self):
"""
Try to break it, the algorithm... !! Guess not mdrfkr.
"""
print("Test 7: Building a more complicated Model")
builder = StaticBuilder("BreakIt")
in1 = builder.addInput(10)
in2 = builder.addInput(20)
enc1 = builder.addTransformInner(3, main_inputs=in1)
enc2 = builder.addTransformInner(5, main_inputs=[in2, enc1])
builder.build()
enc1, enc2 = builder.nodes[enc1], builder.nodes[enc2]
print("enc1._islot_to_itensor", enc1._islot_to_itensor)
print("enc1._islot_to_itensor", enc2._islot_to_itensor)
print("enc1._oslot_to_otensor", enc1._oslot_to_otensor)
print("enc1._oslot_to_otensor", enc2._oslot_to_otensor)
def test_BuildModel5(self):
"""
Try to break it, the algorithm... !! Guess not mdrfkr.
"""
print("\nTest 2: Building a Model with 2 inputs")
builder = StaticBuilder("BreakIt")
in1 = builder.addInput(10)
in2 = builder.addInput(20)
enc1 = builder.addTransformInner(3,
main_inputs=[in1, in2],
node_class=NormalTriLNode)
enc2 = builder.addTransformInner(5, main_inputs=enc1)
builder.build()
enc1, enc2 = builder.nodes[enc1], builder.nodes[enc2]
print("enc1._islot_to_itensor", enc1._islot_to_itensor)
print("enc1._oslot_to_otensor", enc1._oslot_to_otensor)
print("enc2._islot_to_itensor", enc2._islot_to_itensor)
print("enc2._oslot_to_otensor", enc2._oslot_to_otensor)
def test_addInner(self):
"""
Test adding basic Deterministic InnerNode.
NOTE: The DeterministicNode belongs to the class of nodes that starts with a
known final number of outputs.
"""
print("Test 2: Adding InnerNode")
try:
builder = StaticBuilder(scope='Build0')
i1 = builder.addInput(state_size=10, name="In")
enc_name = builder.addTransformInner(state_sizes=3,
main_inputs=i1,
name="In")
except AttributeError:
print("\nCAUGHT! (AttributeError exception) \n"
"Trying to assign the same name to two nodes!")
builder = StaticBuilder(scope='Build0')
i1 = builder.addInput(state_size=10, name="In")
enc_name = builder.addTransformInner(state_size=3, main_inputs=i1)
enc1 = builder.nodes[enc_name]
print('\nNode keys in builder:', list(builder.nodes.keys()))
print("This node's key:", enc_name)
print("Builder adjacency matrix", builder.adj_matrix)
self.assertEqual(enc1.label, 1,
"The label has not been assigned correctly")
self.assertEqual(
builder.num_nodes, 2, "The number of nodes has not been "
"assigned correctly")
self.assertEqual(
enc1.num_declared_outputs, 0, "The number of outputs of the "
"DeterministicNode has not been assigned correctly")
self.assertEqual(
enc1.num_declared_inputs, 0, "The number of inputs of the "
"DeterministicNode has not been assigned correctly")
def test_BuildModel2(self):
"""
Builds a model with 2 inputs. Test concatenation
"""
print("Test 4: Building a Model with concat")
builder = StaticBuilder("Concat")
in1 = builder.addInput(10)
in2 = builder.addInput(20)
enc1 = builder.addTransformInner(3, main_inputs=[in1, in2])
in1, in2, enc1 = builder.nodes[in1], builder.nodes[in2], builder.nodes[
enc1]
builder.build()
print("enc1._islot_to_itensor", enc1._islot_to_itensor)
print("enc1._oslot_to_otensor", enc1._oslot_to_otensor)
def test_BuildModel0(self):
"""
Test building the simplest model possible.
"""
print("Test 3: Building a Basic Model")
builder = StaticBuilder(scope="Basic")
in_name = builder.addInput(10)
enc_name = builder.addTransformInner(3, main_inputs=in_name)
inn, enc = builder.nodes[in_name], builder.nodes[enc_name]
builder.build()
print("enc._islot_to_itensor", enc._islot_to_itensor)
self.assertEqual(inn._oslot_to_otensor['main'].shape.as_list()[-1],
enc._islot_to_itensor[0]['main'].shape.as_list()[-1],
"The input tensors have not been assigned correctly")
def test_train1(self):
"""
"""
print("Test 0:")
dirs = {}
builder = StaticBuilder(scope='vae')
odim = 10
idim = 3
hdim = 16
i1 = builder.addInput(odim, name='Observation', **dirs)
enc0 = builder.addTransformInner(hdim, main_inputs=i1, name='Inner')
enc1 = builder.addTransformInner(idim,
main_inputs=enc0,
name='Recognition',
node_class=NormalTriLNode)
builder.addTransformInner(odim,
main_inputs=enc1,
name='Generative',
node_class=NormalTriLNode)
vae = VariationalAutoEncoder(builder=builder)
# save_on_valid_improvement=True) # OK!
vae.train(dataset, num_epochs=20)
def test_BuildModel1(self):
"""
Test building the simplest stochastic model possible.
"""
print("\nTest 1: Building a Model with a Stochastic Node")
builder = StaticBuilder(scope="BasicNormal")
i1 = builder.addInput(10)
in1 = builder.addTransformInner(3,
main_inputs=i1,
node_class=NormalTriLNode)
builder.build()
inn, enc = builder.nodes[i1], builder.nodes[in1]
self.assertEqual(inn._oslot_to_otensor['main'].shape.as_list()[-1],
enc._islot_to_itensor[0]['main'].shape.as_list()[-1],
"The input tensors have not been assigned correctly")
print("enc._oslot_to_otensor", enc._oslot_to_otensor)
print("enc._islot_to_itensor", enc._islot_to_itensor)
