def train(data_path):
load_data = pd.read_pickle(data_path).iloc[:10000, :]
xs = load_data.iloc[:, 1:]
print(xs.head(2))
print('sample size:', pd.read_pickle(data_path).shape[0])
ys = load_data.a
data = tfnn.Data(xs, ys, name='road_data')
network = tfnn.RegNetwork(xs.shape[1], 1, do_dropout=False)
n_data = network.normalizer.minmax_fit(data)
t_data, v_data = n_data.train_test_split(0.7)
network.add_hidden_layer(100, activator=tfnn.nn.relu, dropout_layer=True)
network.add_output_layer(activator=None, dropout_layer=False)
global_step = tfnn.Variable(0, trainable=False)
# lr = tfnn.train.exponential_decay(0.001, global_step, 2000, 0.9)
optimizer = tfnn.train.AdamOptimizer(0.001)
network.set_optimizer(optimizer, global_step)
evaluator = tfnn.Evaluator(network)
summarizer = tfnn.Summarizer(network, save_path='/tmp/log')
for i in range(10000):
b_xs, b_ys = t_data.next_batch(100, loop=True)
network.run_step(b_xs, b_ys, 0.5)
if i % 1000 == 0:
print(evaluator.compute_cost(v_data.xs, v_data.ys))
summarizer.record_train(b_xs, b_ys, i, 0.5)
summarizer.record_validate(v_data.xs, v_data.ys, i)
network.save()
evaluator.regression_plot_linear_comparison(v_data.xs,
v_data.ys,
continue_plot=True)
network.sess.close()
summarizer.web_visualize()
from tensorflow.examples.tutorials.mnist import input_data
import tfnn
# digits image data from 0 to 9
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# define network properties
network = tfnn.ClfNetwork(input_size=mnist.train.images.shape[1],
output_size=mnist.train.labels.shape[1])
# add hidden layer
network.add_hidden_layer(n_neurons=100, activator=tfnn.nn.relu)
# add output layer
network.add_output_layer(activator=None)
# set optimizer. Default GradientDescent
network.set_optimizer()
# set evaluator for compute the accuracy, loss etc.
evaluator = tfnn.Evaluator(network)
# similar to sklearn, we have fit function
network.fit(mnist.train.images, mnist.train.labels, steps=2000)
# use evaluator to compute accuracy and loss
print('accuracy: ',
evaluator.compute_accuracy(xs=mnist.test.images, ys=mnist.test.labels))
