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lenet_tensor.py
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import numpy as np
import tensor.Variable as var
import tensor.Operator as op
import tensor.Activation as activation
from tensor.util import learning_rate_exponential_decay
import plot
import time
import struct
from glob import glob
import os
VERSION = 'TENSOR_Adam_RELU'
def load_mnist(path, kind='train'):
"""Load MNIST data from `path`"""
images_path = glob('./%s/%s*3-ubyte' % (path, kind))[0]
labels_path = glob('./%s/%s*1-ubyte' % (path, kind))[0]
with open(labels_path, 'rb') as lbpath:
magic, n = struct.unpack('>II',
lbpath.read(8))
labels = np.fromfile(lbpath,
dtype=np.uint8)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack('>IIII',
imgpath.read(16))
images = np.fromfile(imgpath,
dtype=np.uint8).reshape(len(labels), 784)
return images, labels
def inference(x, output_num):
conv1_out = op.Conv2D((5, 5, 1, 12), input_variable=x, name='conv1', padding='VALID').output_variables
relu1_out = activation.Relu(input_variable=conv1_out, name='relu1').output_variables
#dropout1_out = op.DropOut(input_variable=relu1_out, name='dropout1', phase='train', prob=0.7).output_variables
pool1_out = op.MaxPooling(ksize=2, input_variable=relu1_out, name='pool1').output_variables
conv2_out = op.Conv2D((3, 3, 12, 24), input_variable=pool1_out, name='conv2').output_variables
relu2_out = activation.Relu(input_variable=conv2_out, name='relu2').output_variables
#dropout2_out = op.DropOut(input_variable=relu2_out, name='dropout2', phase='train', prob=0.7).output_variables
pool2_out = op.MaxPooling(ksize=2, input_variable=relu1_out, name='pool2').output_variables
fc_out = op.FullyConnect(output_num=output_num, input_variable=pool2_out, name='fc').output_variables
return fc_out
batch_size = 64
global_step = 0
# set method
for k in var.GLOBAL_VARIABLE_SCOPE:
s = var.GLOBAL_VARIABLE_SCOPE[k]
if isinstance(s, var.Variable) and s.learnable:
s.set_method_adam()
img_placeholder = var.Variable((batch_size, 28, 28, 1), 'input')
label_placeholder = var.Variable([batch_size, 1], 'label')
# set train_op
prediction = inference(img_placeholder, 10)
sf = op.SoftmaxLoss(prediction, label_placeholder, 'sf')
images, labels = load_mnist('./data/mnist')
test_images, test_labels = load_mnist('./data/mnist', 't10k')
# save train curve config
loss_collect = []
acc_collect = []
print 'new'
with open('logs/%s_log.txt'%VERSION, 'wb') as logf:
for epoch in range(20):
# random shuffle
order = np.arange(images.shape[0])
np.random.shuffle(order)
_images = images[order]
_labels = labels[order]
# batch
batch_loss = 0
batch_acc = 0
val_acc = 0
val_loss = 0
# train
train_acc = 0
train_loss = 0
for i in range(images.shape[0] / batch_size):
learning_rate = learning_rate_exponential_decay(5e-4, epoch, 0.1, 10)
# feed
img_placeholder.data = _images[i * batch_size:(i + 1) * batch_size].reshape([batch_size, 28, 28, 1])
label_placeholder.data = _labels[i * batch_size:(i + 1) * batch_size]
# forward
_loss = sf.loss.eval()
_prediction = sf.prediction.eval()
batch_loss += _loss
train_loss += _loss
for j in range(batch_size):
if np.argmax(_prediction[j]) == label_placeholder.data[j]:
batch_acc += 1
train_acc += 1
# backward
img_placeholder.diff_eval()
for k in var.GLOBAL_VARIABLE_SCOPE:
s = var.GLOBAL_VARIABLE_SCOPE[k]
if isinstance(s, var.Variable) and s.learnable:
s.apply_gradient(learning_rate=learning_rate, decay_rate=0.0004, batch_size=batch_size)
if isinstance(s, var.Variable):
s.diff = np.zeros(s.shape)
global_step += 1
if i % 50 == 0 and i!= 0:
print time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + \
" %s epoch: %d , batch: %5d , avg_batch_acc: %.4f avg_batch_loss: %.4f learning_rate %f" % (VERSION,epoch,
i, batch_acc / float(
batch_size), batch_loss / batch_size, learning_rate)
logf.write(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + \
" %s epoch: %d , batch: %5d , avg_batch_acc: %.4f avg_batch_loss: %.4f learning_rate %f\n" % (VERSION,epoch,
i, batch_acc / float(
batch_size), batch_loss / batch_size, learning_rate))
loss_collect.append(batch_loss / batch_size)
acc_collect.append(batch_acc / float(batch_size))
batch_loss = 0
batch_acc = 0
print time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime()) + " epoch: %5d , train_acc: %.4f avg_train_loss: %.4f" % (
epoch, train_acc / float(int(images.shape[0]/batch_size)*batch_size), train_loss / images.shape[0])
# validation
for i in range(test_images.shape[0] / batch_size):
img_placeholder.data = test_images[i * batch_size:(i + 1) * batch_size].reshape([batch_size, 28, 28, 1])
label_placeholder.data = test_labels[i * batch_size:(i + 1) * batch_size]
for k in var.GLOBAL_VARIABLE_SCOPE:
s = var.GLOBAL_VARIABLE_SCOPE[k]
if isinstance(s, var.Variable):
s.wait_bp = False
if isinstance(s, op.Operator):
s.wait_forward = True
if isinstance(s,op.Operator) and hasattr(s,'phase'): #== 'train':
s.phase = 'test'
_loss = sf.loss.eval()
_prediction = sf.prediction.eval()
val_loss += _loss
for j in range(batch_size):
if np.argmax(sf.softmax[j]) == label_placeholder.data[j]:
val_acc += 1
print time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + " epoch: %5d , val_acc: %.4f avg_val_loss: %.4f" % (
epoch, val_acc / float(int(test_images.shape[0]/batch_size)*batch_size), val_loss / test_images.shape[0])
for k in var.GLOBAL_VARIABLE_SCOPE:
s = var.GLOBAL_VARIABLE_SCOPE[k]
if isinstance(s, op.Operator) and hasattr(s,'phase') :#== 'test':
s.phase = 'train'