for i, n_unit in enumerate(self.hidden_units):
net = self._fully_connected_linear(net, [current_dimension, n_unit], i)
net = self._build_layer(i, net)
current_dimension = n_unit
appendix = "_final_projection"
fc_shape = self.hidden_units[-1] if self.hidden_units else current_dimension
self._output = self._fully_connected_linear(net, [fc_shape, self.n_class], appendix)
if __name__ == '__main__':
from Util.Util import DataUtil
for generator in (DataUtil.gen_xor, DataUtil.gen_spiral, DataUtil.gen_nine_grid):
x_train, y_train = generator(size=1000, one_hot=False)
x_test, y_test = generator(size=100, one_hot=False)
nn = Basic(model_param_settings={"n_epoch": 200}).scatter2d(x_train, y_train).fit(
x_train, y_train, x_test, y_test, snapshot_ratio=0
).draw_losses().visualize2d(
x_train, y_train, title="Train"
).visualize2d(
x_test, y_test, padding=2, title="Test"
)
for size in (256, 1000, 10000):
(x_train, y_train), (x_test, y_test) = DataUtil.gen_noisy_linear(
size=size, n_dim=2, n_valid=2, test_ratio=100 / size, one_hot=False
)
nn = Basic(model_param_settings={"n_epoch": 200}).scatter2d(x_train, y_train).fit(
x_train, y_train, x_test, y_test, snapshot_ratio=0
).draw_losses().visualize2d(x_train, y_train, title="Train").visualize2d(x_test, y_test, title="Test")
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from Util.Util import DataUtil
from _Dist.NeuralNetworks.c_BasicNN.NN import Basic
from _Dist.NeuralNetworks.e_AdvancedNN.NN import Advanced
from _Dist.NeuralNetworks._Tests.TestUtil import draw_acc
x_cv = y_cv = None
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(one_hot=False)
def block_test(generator, **kwargs):
(x_, y_), (x_test_, y_test_) = generator(**kwargs, one_hot=False)
basic_ = Basic(**base_params).fit(x_, y_, x_test_, y_test_, snapshot_ratio=0)
advanced_params["model_structure_settings"]["use_pruner"] = False
wnd_dndf_ = Advanced(**advanced_params).fit(x_, y_, x_test_, y_test_, snapshot_ratio=0)
advanced_params["model_structure_settings"]["use_pruner"] = True
wnd_dndf_pruned_ = Advanced(**advanced_params).fit(x_, y_, x_test_, y_test_, snapshot_ratio=0)
print("BasicNN ", end="")
basic_.evaluate(x_, y_, x_cv, y_cv, x_test_, y_test_)
print("WnD & DNDF ", end="")
wnd_dndf_.evaluate(x_, y_, x_cv, y_cv, x_test_, y_test_)
print("WnD & DNDF & Pruner ", end="")
wnd_dndf_pruned_.evaluate(x_, y_, x_cv, y_cv, x_test_, y_test_)
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from copy import deepcopy
from Util.Util import DataUtil
from _Dist.NeuralNetworks._Tests.TestUtil import draw_acc
from _Dist.NeuralNetworks.b_TraditionalML.SVM import SVM
from _Dist.NeuralNetworks.f_AutoNN.DistNN import AutoAdvanced
base_params = {"model_param_settings": {"n_epoch": 30, "metric": "acc"}}
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(n_dim=2, n_valid=2, test_ratio=0.01, one_hot=False)
svm = SVM(**deepcopy(base_params)).fit(
x, y, x_test, y_test, snapshot_ratio=0).visualize2d(x_test, y_test)
nn = AutoAdvanced("NoisyLinear", **deepcopy(base_params)).fit(
x, y, x_test, y_test, snapshot_ratio=0).visualize2d(x_test, y_test)
draw_acc(svm, nn)
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from copy import deepcopy
from Util.Util import DataUtil
from _Dist.NeuralNetworks._Tests.TestUtil import draw_acc
from _Dist.NeuralNetworks.f_AutoNN.DistNN import AutoAdvanced
from _Dist.NeuralNetworks.b_TraditionalML.SVM import LinearSVM, AutoLinearSVM, DistLinearSVM
base_params = {"model_param_settings": {"n_epoch": 30, "metric": "acc"}}
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(n_dim=2,
n_valid=2,
test_ratio=0.01,
one_hot=False)
svm = LinearSVM(**deepcopy(base_params)).fit(x,
y,
x_test,
y_test,
snapshot_ratio=0).visualize2d(
x_test, y_test)
nn = AutoAdvanced("NoisyLinear",
**deepcopy(base_params)).fit(x,
y,
x_test,
y_test,
snapshot_ratio=0).visualize2d(
x_test, y_test)
draw_acc(svm, nn)
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
import numpy as np
import matplotlib.pyplot as plt
from Util.Util import DataUtil
from _Dist.NeuralNetworks._Tests.Pruner.Advanced import Advanced
# (x, y), (x_test, y_test), *_ = DataUtil.get_dataset("mnist", "_Data/mnist.txt", n_train=1600, quantized=True)
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(n_dim=100, n_valid=5, one_hot=False)
data_info = {
"numerical_idx": [True] * 100 + [False],
"categorical_columns": []
}
# nn = Advanced(
# "NoisyLinear",
# data_info=data_info,
# model_param_settings={
# "n_epoch": 40
# },
# model_structure_settings={
# "use_wide_network": False,
# "use_pruner": True,
# "pruner_params": {
# "prune_method": "surgery",