def test_addDirectedLinks(self):
"""
Test adding DirectedLinks
"""
print("\nTest 3: Adding DirectedLinks")
builder = StaticBuilder()
in1 = builder.addInput(10, name="In")
enc1 = builder.addInner(3, name="Det")
out1 = builder.addOutput(name="Out")
builder.addDirectedLink(in1, enc1)
builder.addDirectedLink(enc1, out1)
enc1 = builder.nodes[enc1]
print('\nNode keys in builder:', list(builder.nodes.keys()))
self.assertEqual(
builder.num_nodes, 3, "The number of nodes has not been "
"assigned correctly")
self.assertEqual(
enc1.num_declared_outputs, 1, "The number of outputs of the "
"DeterministicNode has not been assigned correctly")
self.assertEqual(
enc1.num_declared_inputs, 1, "The number of inputs of the "
"DeterministicNode has not been assigned correctly")
self.assertIn(
1, builder.adj_list[0], "Node 1 has not been added to the "
"adjacency list of Node 0")
self.assertIn(
2, builder.adj_list[1], "Node 2 has not been added to the "
"adjacency list of Node 1")
def test_BuildModel0(self):
"""
Test building the simplest model possible.
"""
print("\nTest 4: Building a Basic Model")
builder = StaticBuilder(scope="Basic")
in_name = builder.addInput(10)
enc_name = builder.addInner(3)
out_name = builder.addOutput()
builder.addDirectedLink(in_name, enc_name)
builder.addDirectedLink(enc_name, out_name)
self.assertEqual(
builder.num_nodes, 3, "The number of nodes has not been "
"assigned correctly")
builder.build()
inn, enc, out = (builder.nodes[in_name], builder.nodes[enc_name],
builder.nodes[out_name])
self.assertEqual(inn._oslot_to_otensor[0].shape.as_list()[-1],
enc._islot_to_itensor[0].shape.as_list()[-1],
"The input tensors have not been assigned correctly")
self.assertEqual(enc._oslot_to_otensor[0].shape.as_list()[-1],
out._islot_to_itensor[0].shape.as_list()[-1],
"The input tensors have not been assigned correctly")
def test_train_custom_builder(self):
"""
"""
print("Running Test 0\n")
x = 10.0 * np.random.randn(100, 2)
y = x[:,
0:1] + 1.5 * x[:,
1:] # + 3*x[:,1:]**2 + 0.5*np.random.randn(100,1)
dataset = {'train_features': x, 'train_input_response': y}
# Define a builder.
# More control on the directives of each node
builder = StaticBuilder()
enc_dirs = {
'num_layers': 2,
'num_nodes': 128,
'activation': 'leaky_relu',
'net_grow_rate': 1.0
}
input_dim, output_dim = 2, 1
in0 = builder.addInput(input_dim, name="features")
enc1 = builder.addInner(1, 10, directives=enc_dirs)
enc2 = builder.addInner(1, output_dim, directives=enc_dirs)
out0 = builder.addOutput(name="prediction")
builder.addDirectedLink(in0, enc1)
builder.addDirectedLink(enc1, enc2)
builder.addDirectedLink(enc2, out0)
# Pass the builder object to the Regression Model
reg = Regression(builder=builder)
reg.build()
reg.visualize_model_graph("two_encoders.png")
reg.train(dataset, num_epochs=50)
def test_CallCustom(self):
"""
Test, calling a CustomNode
"""
print("\nTest 1: Build")
builder = StaticBuilder("MyModel")
cust = builder.createCustomNode(num_inputs=1,
num_outputs=1,
name="Custom")
cust_in1 = cust.addInner(3)
cust_in2 = cust.addInner(4)
cust.addDirectedLink(cust_in1, cust_in2, islot=0)
cust.declareIslot(islot=0, innernode_name=cust_in1, inode_islot=0)
cust.declareOslot(oslot='main',
innernode_name=cust_in2,
inode_oslot='main')
in1 = builder.addInput(10)
builder.addDirectedLink(in1, cust, islot=0)
builder.build()
X = tf.placeholder(tf.float32, [1, 10], 'X')
Y = cust(X) # pylint: disable=unpacking-non-sequence
print('Y', Y)
self.assertEqual(Y[0].shape[-1], 4, "")
def test_BuildModel2(self):
"""
Builds a model with 2 inputs. Test ConcatNode
"""
print("\nTest 6: Building a Model with Concat")
builder = StaticBuilder("Concat")
in1 = builder.addInput(10)
in2 = builder.addInput(20)
enc1 = builder.addInner(3, num_islots=2)
out1 = builder.addOutput()
builder.addDirectedLink(in1, enc1, islot=0)
builder.addDirectedLink(in2, enc1, islot=1)
builder.addDirectedLink(enc1, out1)
builder.build()
def test_BuildModel1(self):
"""
Test building a model with 2 outputs. Test Cloning an output
"""
print("\nTest 5: Building a Model with cloning")
builder = StaticBuilder("Clone")
in1 = builder.addInput(10)
enc1 = builder.addInner(3)
out1 = builder.addOutput(name="Out1")
out2 = builder.addOutput(name="Out2")
builder.addDirectedLink(in1, enc1)
builder.addDirectedLink(enc1, out1)
builder.addDirectedLink(enc1, out2)
builder.build()
def test_merge_build1(self):
"""
Test Merge Node build
"""
builder = StaticBuilder(scope='Main')
i1 = builder.addInput([[1]])
in1 = builder.addInner([[3]], node_class=NormalTriLNode)
in2 = builder.addInner([[3]], node_class=NormalTriLNode)
builder.addDirectedLink(i1, in1, islot=0)
builder.addDirectedLink(i1, in2, islot=0)
m1 = builder.addMergeNode([in1, in2],
merge_class=MergeNormals)
builder.build()
m1 = builder.nodes[m1]
self.assertIn('loc', m1._oslot_to_otensor)
self.assertIn('main', m1._oslot_to_otensor)
self.assertIn('cov', m1._oslot_to_otensor)
def test_add_encoder1(self):
"""
Test build
"""
tf.reset_default_graph()
builder = StaticBuilder("MyModel")
cust = builder.createCustomNode(1, 1, name="Custom")
cust_in1 = cust.addInner(3)
cust_in2 = cust.addInner(4)
cust.addDirectedLink(cust_in1, cust_in2)
cust.commit()
in1 = builder.addInput(10)
o1 = builder.addOutput()
builder.addDirectedLink(in1, cust)
builder.addDirectedLink(cust, o1)
builder.build()
def test_CallDeterministic(self):
"""
Test Calling a DeterministicNode
"""
print("\nTest 0: Calling a Deterministic Node")
builder = StaticBuilder(scope="Basic")
in_name = builder.addInput(10)
enc_name = builder.addInner(3)
builder.addDirectedLink(in_name, enc_name, islot=0)
self.assertEqual(
builder.num_nodes, 2, "The number of nodes has not been "
"assigned correctly")
builder.build()
X = tf.placeholder(tf.float32, [1, 10], 'X')
enc = builder.nodes[enc_name]
Y = enc((X))
print('Y', Y)
self.assertEqual(Y.shape[-1], 3, "")
def test_train_custom_node2(self):
"""
Test commit
"""
print("Test 2: with CustomNode\n")
input_dim = 2
x = 10.0 * np.random.randn(100, input_dim)
y = x[:,
0:1] + 1.5 * x[:,
1:] # + 3*x[:,1:]**2 + 0.5*np.random.randn(100,1)
dataset = {'train_features': x, 'train_input_response': y}
builder = StaticBuilder("MyModel")
enc_dirs = {
'num_layers': 2,
'num_nodes': 128,
'activation': 'leaky_relu',
'net_grow_rate': 1.0
}
in0 = builder.addInput(input_dim, name='features')
cust = builder.createCustomNode(1, 1, name="Custom")
cust_in1 = cust.addInner(3, directives=enc_dirs)
cust_in2 = cust.addInner(1, directives=enc_dirs)
cust.addDirectedLink(cust_in1, cust_in2)
cust.addInput(islot=0, inode_name=cust_in1, inode_islot=0)
cust.addOutput(oslot=0, inode_name=cust_in2, inode_oslot=0)
cust.commit()
out0 = builder.addOutput(name='prediction')
builder.addDirectedLink(in0, cust)
builder.addDirectedLink(cust, out0)
# Pass the builder object to the Regression Model
reg = Regression(input_dim=2, output_dim=1, builder=builder)
reg.build()
reg.visualize_model_graph("two_encoders.png")
reg.train(dataset, num_epochs=50)
def test_BuildModel3(self):
"""
Try to break it, the algorithm... !! Guess not mdrfkr.
"""
print("\nTest 7: Building a more complicated Model")
builder = StaticBuilder("BreakIt")
in1 = builder.addInput(10)
in2 = builder.addInput(20)
enc1 = builder.addInner(3)
enc2 = builder.addInner(5, num_islots=2)
out1 = builder.addOutput()
out2 = builder.addOutput()
builder.addDirectedLink(in1, enc1)
builder.addDirectedLink(in2, enc2, islot=0)
builder.addDirectedLink(enc1, enc2, islot=1)
builder.addDirectedLink(enc1, out1)
builder.addDirectedLink(enc2, out2)
builder.build()
def test_train_custom_builder2(self):
"""
Build a custom Regression model whose Model graph has the rhombic design
"""
print("Running Test 1\n")
x = 10.0 * np.random.randn(100, 2)
y = x[:,
0:1] + 1.5 * x[:,
1:] # + 3*x[:,1:]**2 + 0.5*np.random.randn(100,1)
dataset = {'train_features': x, 'train_input_response': y}
# Define a builder, control the graph
# Like a lot more control...
builder = StaticBuilder()
enc_dirs = {
'num_layers': 2,
'num_nodes': 128,
'activation': 'leaky_relu',
'net_grow_rate': 1.0
}
input_dim = 2
in0 = builder.addInput(input_dim, name="features")
enc1 = builder.addInner(10, num_islots=1, directives=enc_dirs)
enc2 = builder.addInner(10, num_islots=1, directives=enc_dirs)
enc3 = builder.addInner(1,
num_islots=2,
num_layers=1,
activation='linear')
out0 = builder.addOutput(name="prediction")
builder.addDirectedLink(in0, enc1)
builder.addDirectedLink(in0, enc2)
builder.addDirectedLink(enc1, enc3, islot=0)
builder.addDirectedLink(enc2, enc3, islot=1)
builder.addDirectedLink(enc3, out0)
# Pass the builder object to the Regression Model
reg = Regression(input_dim=2, output_dim=1, builder=builder)
reg.build()
reg.visualize_model_graph("two_encoders.png")
reg.train(dataset, num_epochs=50)