def test_model_vars_after_run(self):
args = parser.parse_args()
json_path = os.path.join(args.params_dir, 'image_segmentation_params.json')
assert os.path.isfile(json_path), "No json configuration file found at {}".format(json_path)
dataset_params = param_manager.DatasetParams()
dataset_params.update(json_path)
dataset_dict = dataset_params.dict
dataset = parse_image_seg.Dataset(dataset_dict)
checkpoint_path = "./results/unitest2.ckpt"
model = NeuralNet(dataset, self.logger, self.params)
with model:
model.build_model()
model.train_model()
self.compare_to_ckpt(model, checkpoint_path)
from model import NeuralNet
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
# Generate a dataset and plot it
np.random.seed(0)
X, Y = sklearn.datasets.make_moons(200, noise=0.20)
plt.scatter(X[:, 0], X[:, 1], s=40, c=Y, cmap=plt.cm.Spectral)
# plt.savefig("data.png")
# Build model with 3 hidden layers
model = NeuralNet([2, 3, 2], activation_function='tanh', print_cost=True)
model.build_model(X, Y)
# Plot decision boundary
