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test_neural_network_regression.py
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import os
from os.path import join as pjoin
import numpy as np
from pdb import set_trace
import math
import scipy
# import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from neural_network_regression import eval_activation_func, RegressionNeuralNetwork, eval_loss_func
def test_layer_sizes():
print("Function test_layer_sizes")
optimizer_params = {
"learning_rate": 0.01,
}
print('1st net: RegressionNeuralNetwork(3, [2, 4, 5], 6, ["sigmoid", "sigmoid", "sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(3, [2, 4, 5], 6, ["sigmoid", "sigmoid", "sigmoid", "linear"], optimizer_params)
net.print_layer_sizes()
print('2nd net: RegressionNeuralNetwork(3, [10, 4, 5], 8, ["sigmoid", "sigmoid", "sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(3, [10, 4, 5], 8, ["sigmoid", "sigmoid", "sigmoid", "linear"], optimizer_params)
net.print_layer_sizes()
print('3rd net: RegressionNeuralNetwork(1, [4, 4], 1, ["sigmoid", "sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [4, 4], 1, ["sigmoid", "sigmoid", "linear"], optimizer_params)
net.print_layer_sizes()
print("")
def test_eval_1():
print("Function test_eval_1")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[-0.27],
[-0.41],
])
net.layer_list[0]["bias"] = np.array(
[[-0.48],
[-0.13],
])
net.layer_list[1]["weight"] = np.array(
[[0.09, -0.17],
])
net.layer_list[1]["bias"] = np.array(
[[0.48],
])
x_in = np.array([1])
net.eval(x_in)
out_list = net.out_list
for layer_idx, out in enumerate(out_list):
print("Layer %d:" % (layer_idx+1))
print(out)
""" Correct output:
Layer 1:
[[0.3208213 ]
[0.36818758]]
Layer 2:
[[0.44628203]]
"""
print("")
def test_eval_2():
print("Function test_eval_2")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[1],
])
net.layer_list[0]["bias"] = np.array(
[[1],
])
net.layer_list[1]["weight"] = np.array(
[[-2],
])
net.layer_list[1]["bias"] = np.array(
[[1],
])
x_in = np.array([1])
net.eval(x_in)
out_list = net.out_list
for layer_idx, out in enumerate(out_list):
print("Layer %d:" % (layer_idx+1))
print(out)
""" Correct output:
Layer 1:
[[0.88079708]]
Layer 2:
[[-0.76159416]]
"""
print("")
def test_eval_3():
print("Function test_eval_3")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [1], 1, ["tanh", "tanh"], 0.01)')
net = RegressionNeuralNetwork(1, [1], 1, ["tanh", "tanh"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[-1],
])
net.layer_list[0]["bias"] = np.array(
[[1],
])
net.layer_list[1]["weight"] = np.array(
[[-2],
])
net.layer_list[1]["bias"] = np.array(
[[1],
])
x_in = np.array([-1])
net.eval(x_in)
out_list = net.out_list
for layer_idx, out in enumerate(out_list):
print("Layer %d:" % (layer_idx+1))
print(out)
""" Correct output:
Layer 1:
[[0.96402758]]
Layer 2:
[[-0.72968586]]
"""
print("")
def test_loss_func_1():
print("Function test_loss_func_1")
y_true = np.array(
[
[1],
[0],
]
)
y_pred = np.array(
[
[0.8],
[0.6],
]
)
loss = eval_loss_func(y_true, y_pred, "mse")
print(y_true)
print(y_pred)
print(loss)
print("")
def test_loss_func_2():
print("Function test_loss_func_2")
y_true = np.array(
[
[1, 0, 0],
[0, 0, 1],
]
)
y_pred = np.array(
[
[0.8, 0.1, 0.1],
[0.6, 0.1, 0.3],
]
)
loss = eval_loss_func(y_true, y_pred, "mse")
print(y_true)
print(y_pred)
print(loss)
print("")
def test_backprop_1():
print("Function test_backprop_1")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[-0.27],
[-0.41],
])
net.layer_list[0]["bias"] = np.array(
[[-0.48],
[-0.13],
])
net.layer_list[1]["weight"] = np.array(
[[0.09, -0.17],
])
net.layer_list[1]["bias"] = np.array(
[[0.48],
])
x_in = np.array(
[1],
)
net.eval(x_in)
out_list = net.out_list
y_true = np.array(
[1.7071067811865475],
)
net.backprop(y_true)
for layer_idx in reversed(range(0, net.n_layer)):
print("Sensitivity of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["sensitivity"])
""" Correct output:
Sensitivity of layer 2:
[-2.52164951]
Sensitivity of layer 1:
[[-0.04945093]
[ 0.09972199]]
"""
print("")
def test_backprop_2():
print("Function test_backprop_2")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], optimizer_params)
net = RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[1],
])
net.layer_list[0]["bias"] = np.array(
[[1],
])
net.layer_list[1]["weight"] = np.array(
[[-2],
])
net.layer_list[1]["bias"] = np.array(
[[1],
])
x_in = np.array(
[1],
)
net.eval(x_in)
out_list = net.out_list
y_true = np.array(
[1.0],
)
net.backprop(y_true)
for layer_idx in reversed(range(0, net.n_layer)):
print("Sensitivity of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["sensitivity"])
""" Correct output:
Sensitivity of layer 2:
[-3.52318831]
Sensitivity of layer 1:
[0.73982435]
"""
print("")
def test_update_gradient_1():
print("Function test_update_gradient_1")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [2], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[-0.27],
[-0.41],
])
net.layer_list[0]["bias"] = np.array(
[[-0.48],
[-0.13],
])
net.layer_list[1]["weight"] = np.array(
[[0.09, -0.17],
])
net.layer_list[1]["bias"] = np.array(
[[0.48],
])
x_in = np.array(
[1],
)
net.eval(x_in)
out_list = net.out_list
y_true = np.array(
[1.7071067811865475],
)
net.backprop(y_true)
net.update_gradient(y_true)
for layer_idx in reversed(range(0, net.n_layer)):
print("Weight gradient of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["weight_grad"])
print("Bias gradient of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["bias_grad"])
""" Correct output:
Weight gradient of layer 2:
[[-0.80899887 -0.92844004]]
Bias gradient of layer 2:
[-2.52164951]
Weight gradient of layer 1:
[[-0.04945093]
[ 0.09972199]]
Bias gradient of layer 1:
[-0.04945093 0.09972199]
"""
print("")
def test_update_gradient_2():
print("Function test_update_gradient_2")
optimizer_params = {
"learning_rate": 0.01,
}
print('Net: RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], 0.01)')
net = RegressionNeuralNetwork(1, [1], 1, ["sigmoid", "linear"], optimizer_params)
net.layer_list[0]["weight"] = np.array(
[[1],
])
net.layer_list[0]["bias"] = np.array(
[[1],
])
net.layer_list[1]["weight"] = np.array(
[[-2],
])
net.layer_list[1]["bias"] = np.array(
[[1],
])
x_in = np.array(
[1],
)
net.eval(x_in)
out_list = net.out_list
y_true = np.array(
[1.0],
)
net.backprop(y_true)
net.update_gradient(y_true)
for layer_idx in reversed(range(0, net.n_layer)):
print("Weight gradient of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["weight_grad"])
print("Bias gradient of layer %d:" % (layer_idx+1))
print(net.layer_list[layer_idx]["bias_grad"])
""" Correct output:
Weight gradient of layer 2:
[[-3.10321397]]
Bias gradient of layer 2:
[-3.52318831]
Weight gradient of layer 1:
[[0.73982435]]
Bias gradient of layer 1:
[0.73982435]
"""
print("")
def run_tests():
test_layer_sizes()
test_eval_1()
test_eval_2()
test_eval_3()
test_loss_func_1()
test_loss_func_2()
test_backprop_1()
test_backprop_2()
test_update_gradient_1()
test_update_gradient_2()
def main():
run_tests()
if __name__ == "__main__":
main()