def train(self, num_epochs=10):
data = Dataloader(batchsize=self.batch_size)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_X, _ = data.getTrainData()
num_tr_iter = int(train_X.shape[0] / self.batch_size)
global_step = 0
Loss = []
Acc = []
for epoch in range(num_epochs):
print("-------------------------------")
print("Training epoch: {}".format(epoch + 1))
data.i = 0
acc = np.zeros(num_tr_iter)
loss = np.zeros(num_tr_iter)
for iteration in range(num_tr_iter):
global_step += 1
x_batch, y_batch = data.nextBatch()
# Run optimization op (backprop)
feed_dict_batch = {
self.Model.inputs: x_batch,
self.Model.labels: y_batch,
}
sess.run(self.Model.train_op, feed_dict=feed_dict_batch)
if iteration % 3 == 0:
# Calculate and display the batch loss and accuracy
loss_batch, acc_batch = sess.run(
[self.Model.loss, self.Model.accuracy],
feed_dict=feed_dict_batch,
)
print(
"iter {0:3d}:\t Loss={1:.2f},\tTraining Accuracy={2:.01%}"
.format(iteration, loss_batch, acc_batch))
acc[iteration] = acc_batch
loss[iteration] = loss_batch
x_valid, y_valid = data.getValiData()
val_loss, val_acc = sess.run(
[self.Model.loss, self.Model.accuracy],
feed_dict={
self.Model.inputs: x_valid,
self.Model.labels: y_valid
},
)
print(
"Epoch: {0}, validation loss: {1:.2f}, validation accuracy: {2:.01%}"
.format(epoch + 1, val_loss, val_acc))
acc, loss = np.mean(acc), np.mean(loss)
Loss.append(loss)
Acc.append(acc)
x_test, y_test = data.getTestData()
feed_dict_test = {
self.Model.inputs: x_test,
self.Model.labels: y_test
}
test_loss, test_acc = sess.run(
[self.Model.loss, self.Model.accuracy],
feed_dict=feed_dict_test)
print("--------------------")
print("Test loss: {0:.2f}, test accuracy: {1:.01%}".format(
test_loss, test_acc))
return Loss, Acc
import numpy as np
import tensorflow as tf
from DataLoader import Dataloader
data = Dataloader(batchsize=32)
train_X, train_Y = data.getTrainData()
test_X, test_Y = data.getTestData()
valid_X, valid_Y = data.getValiData()
label_num = train_Y.shape[0] * train_Y.shape[1] + test_Y.shape[
0] * test_Y.shape[1] + valid_Y.shape[0] * valid_Y.shape[1]
label_1_num = np.sum(train_Y) + np.sum(test_Y) + np.sum(valid_Y)
pass