def train_minibatch():
generator = MiniBatchGenerator(861, x_mapper, y_mapper)
generator.split_train_test()
"""
print('load train mini-batch')
while True:
X, y = generator.load_next_train_batch(10)
if X is None:
break
print(y)
X_to_train, y_to_train = preprocess(X, y)
print(X_to_train.shape)
print(y_to_train.shape)
break
sess, y_predict, x_train, y_train = models.train_nn(NUM_CATEGORIES,
X_to_train, y_to_train,
layers=(256, 256),
iterations=1000)
print('load test mini-batch')
while True:
X, y = generator.load_next_test_batch(10)
if X is None:
break
X_to_test, y_to_test = preprocess(X, y)
correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_train, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x_train: X_to_test, y_train: y_to_test}))
correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_train, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#print(sess.run(accuracy, feed_dict={x_train: X_to_train, y_train: y_to_train}))
print(sess.run(accuracy, feed_dict={x_train: X_to_test, y_train: y_to_test}))
"""
model = MLP(NUM_CATEGORIES,
min_pc,
layers=(256, 256, 256),
learning_rate=0.0005)
model.start_session()
X_test, y_test = generator.load_next_test_batch(1000)
X_test, y_test = preprocess(X_test, y_test)
#sess = None
for iteration in range(1000):
generator.reset()
while True:
X_train, y_train = generator.load_next_train_batch(10)
if X_train is None:
break
X_train, y_train = preprocess(X_train, y_train)
model.train(X_train, y_train)
if iteration % 100 == 0:
print('loss = ', model.calculate_loss())
# print('train acc = ', model.calculate_accuracy(X_train, y_train))
print('test acc = ', model.calculate_accuracy(X_test, y_test))
print("\n---\n")
