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_train_custom_node2(self):
"""
Test a custom Regression model with a CustomNode
"""
print("Test 2: with CustomNode\n")
builder = StaticBuilder("MyModel")
enc_dirs = {
'numlayers': 2,
'numnodes': 128,
'activations': 'leaky_relu',
'netgrowrate': 1.0
}
input_dim = 2
in0 = builder.addInput(input_dim, name='Features')
cust = builder.createCustomNode(inputs=[in0],
num_outputs=1,
name="Prediction")
cust_in1 = cust.addTransformInner(3,
main_inputs=[0],
directives=enc_dirs)
cust_in2 = cust.addTransformInner(1,
main_inputs=cust_in1,
directives=enc_dirs)
cust.declareOslot(oslot=0,
innernode_name=cust_in2,
inode_oslot_name='main')
reg = Regression(builder=builder, input_dim=2, output_dim=1)
# save_on_valid_improvement=True) # ok!
reg.train(dataset, num_epochs=10)
def test_train(self):
"""
Test train
"""
print("Test 0: Regression train")
dc = Regression(input_dim=2,
output_dim=1,
save_on_valid_improvement=False) # ok!
dc.train(dataset, num_epochs=20) # train
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_init(self):
"""
Test initialization
"""
print("\nTest 0: Regression initialization")
Regression(input_dim=10,
output_dim=1)
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)
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_train(self):
"""
Test train
"""
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)
xtrain, xvalid, ytrain, yvalid = x[:80], x[80:], y[:80], y[80:]
train_dataset = {'train_features': xtrain, 'train_response': ytrain}
dc = Regression(input_dim=2, output_dim=1)
dc.build()
dc.train(train_dataset, num_epochs=50)
dataset = {'features': xvalid}
Ypred = dc.sample(dataset, islot=0)
print("Ypred", list(zip(Ypred.tolist(), yvalid.tolist())))
def test_build(self):
"""
Test build
"""
model = Regression(input_dim=10, output_dim=1)
model.build()