Python Tensorflow正在收敛，但预测不好
前几天我发布了一个类似的问题，但后来我对发现的错误进行了编辑，错误预测的问题依然存在 我有两个网络——一个有3个conv层，另一个有3个conv层，然后是3个deconv层。两者都采用200x200的输入图像。输出的分辨率是相同的200x200，但它有两个分类（0或1——这是一个分段网络），因此网络预测维度是200x200x2（加上批次大小）。让我们来谈谈具有deconv层的网络 奇怪的是。。。在10次训练中，可能有3次会趋于一致。其他7个将发散至0.0的精度 conv和deconv层由ReLu激活。优化器做了一些奇怪的事情。当我在每次训练迭代后打印预测时，值的大小开始变大——考虑到它们都通过了ReLu，这是正确的——但在每次迭代后，值变小，直到它们大约在0到2之间。我随后将它们通过一个sigmoid函数（Python Tensorflow正在收敛，但预测不好,python,machine-learning,neural-network,artificial-intelligence,tensorflow,Python,Machine Learning,Neural Network,Artificial Intelligence,Tensorflow,前几天我发布了一个类似的问题，但后来我对发现的错误进行了编辑，错误预测的问题依然存在 我有两个网络——一个有3个conv层，另一个有3个conv层，然后是3个deconv层。两者都采用200x200的输入图像。输出的分辨率是相同的200x200，但它有两个分类（0或1——这是一个分段网络），因此网络预测维度是200x200x2（加上批次大小）。让我们来谈谈具有deconv层的网络 奇怪的是。。。在10次训练中，可能有3次会趋于一致。其他7个将发散至0.0的精度 conv和deconv层由ReLu
sigmoid\u cross\u entropy\u wight\u logitsAccuracy = 0.508033
[[[[ 1. 0.]
[ 0. 1.]
[ 0. 0.]
...,
[ 1. 0.]
[ 1. 1.]
[ 1. 0.]]
[[ 0. 1.]
[ 1. 1.]
[ 0. 0.]
...,
[ 1. 1.]
[ 1. 1.]
[ 0. 1.]]
def conv_nettf.nn.sigmoid（pred）sigmoid\u cross\u entropy\u with\u logitspredimport tensorflow as tf
import pdb
import numpy as np
from numpy import genfromtxt
from PIL import Image
# Parameters
learning_rate = 0.001
training_iters = 10000
batch_size = 10
display_step = 1
# Network Parameters
n_input = 200 # MNIST data input (img shape: 28*28)
n_output = 40000
n_classes = 2 # MNIST total classes (0-9 digits)
#n_input = 200
dropout = 0.75 # Dropout, probability to keep units
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input, n_input])
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
def convert_to_2_channel(x, batch_size):
#assume input has dimension (batch_size,x,y)
#output will have dimension (batch_size,x,y,2)
output = np.empty((batch_size, 200, 200, 2))
temp_arr1 = np.empty((batch_size, 200, 200))
temp_arr2 = np.empty((batch_size, 200, 200))
for i in xrange(batch_size):
for j in xrange(3):
for k in xrange(3):
if x[i][j][k] == 1:
temp_arr1[i][j][k] = 1
temp_arr2[i][j][k] = 0
else:
temp_arr1[i][j][k] = 0
temp_arr2[i][j][k] = 1
for i in xrange(batch_size):
for j in xrange(200):
for k in xrange(200):
for l in xrange(2):
if l == 0:
output[i][j][k][l] = temp_arr1[i][j][k]
else:
output[i][j][k][l] = temp_arr2[i][j][k]
return output
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 200, 200, 1])
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
#conv1 = tf.nn.local_response_normalization(conv1)
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
#conv2 = tf.nn.local_response_normalization(conv2)
conv2 = maxpool2d(conv2, k=2)
# Convolution Layer
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# # Max Pooling (down-sampling)
#conv3 = tf.nn.local_response_normalization(conv3)
conv3 = maxpool2d(conv3, k=2)
temp_batch_size = tf.shape(x)[0]
output_shape = [temp_batch_size, 50, 50, 64]
conv4 = tf.nn.conv2d_transpose(conv3, weights['wdc1'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv4 = tf.nn.bias_add(conv4, biases['bdc1'])
conv4 = tf.nn.relu(conv4)
# conv4 = tf.nn.local_response_normalization(conv4)
# output_shape = tf.pack([temp_batch_size, 100, 100, 32])
output_shape = [temp_batch_size, 100, 100, 32]
conv5 = tf.nn.conv2d_transpose(conv4, weights['wdc2'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv5 = tf.nn.bias_add(conv5, biases['bdc2'])
conv5 = tf.nn.relu(conv5)
# conv5 = tf.nn.local_response_normalization(conv5)
# output_shape = tf.pack([temp_batch_size, 200, 200, 1])
output_shape = [temp_batch_size, 200, 200, 2]
conv6 = tf.nn.conv2d_transpose(conv5, weights['wdc3'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv6 = tf.nn.bias_add(conv6, biases['bdc3'])
conv6 = tf.nn.relu(conv6)
# pdb.set_trace()
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv6, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
return (tf.add(tf.matmul(fc1, weights['out']), biases['out']))# Store layers weight & bias
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1' : tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2' : tf.Variable(tf.random_normal([5, 5, 32, 64])),
# 5x5 conv, 32 inputs, 64 outputs
'wc3' : tf.Variable(tf.random_normal([5, 5, 64, 128])),
'wdc1' : tf.Variable(tf.random_normal([2, 2, 64, 128])),
'wdc2' : tf.Variable(tf.random_normal([2, 2, 32, 64])),
'wdc3' : tf.Variable(tf.random_normal([2, 2, 2, 32])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([80000, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, 80000]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([128])),
'bdc1': tf.Variable(tf.random_normal([64])),
'bdc2': tf.Variable(tf.random_normal([32])),
'bdc3': tf.Variable(tf.random_normal([2])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([80000]))
}
# Construct model
pred = conv_net(x, weights, biases, keep_prob)
pred = tf.reshape(pred, [-1,n_input,n_input,n_classes])
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
# cost = (tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(0,tf.cast(tf.sub(tf.nn.sigmoid(pred),y), tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
summary = tf.train.SummaryWriter('/tmp/logdir/', sess.graph)
step = 1
from tensorflow.contrib.learn.python.learn.datasets.scroll import scroll_data
data = scroll_data.read_data('/home/kendall/Desktop/')
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = data.train.next_batch(batch_size)
# Run optimization op (backprop)
batch_x = batch_x.reshape((batch_size, n_input, n_input))
batch_y = batch_y.reshape((batch_size, n_input, n_input))
batch_y = convert_to_2_channel(batch_y, batch_size) #converts the 200x200 ground truth to a 200x200x2 classification
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
keep_prob: dropout})
#measure prediction
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: 1.})
print prediction
if step % display_step == 0:
# Calculate batch loss and accuracdef conv_net(x, weights, biases, dropout):
save_path = "model.ckpt"
saver.save(sess, save_path)
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: dropout})
print "Accuracy = " + str(acc)
if acc > 0.73:
break
step += 1
print "Optimization Finished!"
#make prediction
im = Image.open('/home/kendall/Desktop/HA900_frames/frame0035.tif')
batch_x = np.array(im)
# pdb.set_trace()
batch_x = batch_x.reshape((1, n_input, n_input))
batch_x = batch_x.astype(float)
pdb.set_trace()
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: dropout})
print prediction
arr1 = np.empty((n_input,n_input))
arr2 = np.empty((n_input,n_input))
for i in xrange(n_input):
for j in xrange(n_input):
for k in xrange(2):
if k == 0:
arr1[i][j] = (prediction[0][i][j][k])
else:
arr2[i][j] = (prediction[0][i][j][k])
# prediction = np.asarray(prediction)
# prediction = np.reshape(prediction, (200,200))
# np.savetxt("prediction.csv", prediction, delimiter=",")
np.savetxt("prediction1.csv", arr1, delimiter=",")
np.savetxt("prediction2.csv", arr2, delimiter=",")
# np.savetxt("prediction2.csv", arr2, delimiter=",")
# Calculate accuracy for 256 mnist test images
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: data.test.images[:256],
y: data.test.labels[:256],
keep_prob: 1.})
将tensorflow导入为tf
导入pdb
将numpy作为np导入
从numpy导入genfromtxt
从PIL导入图像
#参数
学习率=0.001
培训费用=10000
批量大小=10
显示步骤=1
#网络参数
n#u输入=200#m列表数据输入（img形状：28*28）
n_输出=40000
n#u类=2#n列出总类数（0-9位）
#n_输入=200
辍学率=0.75#辍学率，保留单位的概率
#tf图形输入
x=tf.placeholder（tf.float32，[None，n\u输入，n\u输入]）
y=tf.placeholder（tf.float32，[None，n_输入，n_输入，n_类]）
keep_prob=tf.placeholder（tf.float32）#辍学（keep probability）
def convert_至_2_通道（x，批次大小）：
#假设输入有维度（批次大小，x，y）
#输出将具有维度（批次大小，x，y，2）
输出=np.空（（批次大小，200，200，2））
温度arr1=np.空（（批次大小，200200））
温度arr2=np.空（（批次大小，200）
对于X范围内的i（批次尺寸）：
对于X范围内的j（3）：
对于X范围内的k（3）：
如果x[i][j][k]==1：
温度arr1[i][j][k]=1
温度arr2[i][j][k]=0
其他：
温度arr1[i][j][k]=0
温度arr2[i][j][k]=1
对于X范围内的i（批次尺寸）：
对于X范围内的j（200）：
对于X范围内的k（200）：
对于X范围内的l（2）：
如果l==0：
输出[i][j][k][l]=temp_arr1[i][j][k]
其他：
输出[i][j][k][l]=temp_arr2[i][j][k]
返回输出
#为简单起见，创建一些包装
def conv2d（x，W，b，步幅=1）：
#Conv2D包装，带有偏置和relu激活
x=tf.nn.conv2d（x，W，步幅=[1，步幅，步幅，1]，padding='SAME'）
x=tf.nn.bias_add（x，b）
返回tf.nn.relu（x）
def maxpool2d（x，k=2）：
#MaxPool2D包装器
返回tf.nn.max_pool（x，ksize=[1，k，k，1]，步长=[1，k，k，1]，
填充（“相同”）
#创建模型
def conv_净值（x、重量、偏差、损耗）：
#重塑输入图片
x=tf.重塑（x，shape=[-12002001]）
以tf.name_scope（“conv1”）作为作用域：
#卷积层
conv1=conv2d（x，权重['wc1']，偏差['bc1']）
#最大池（下采样）
#conv1=tf.nn.local\u response\u normalization（conv1）
conv1=maxpool2d（conv1，k=2）
#卷积层
以tf.name_scope（“conv2”）作为作用域：
conv2=conv2d（conv1，权重['wc2']，偏差['bc2']）
#最大池（下采样）
#conv2=tf.nn.local\u response\u normalization（conv2）
conv2=maxpool2d（conv2，k=2）
#卷积层
以tf.name_scope（“conv3”）作为作用域：
conv3=conv2d（conv2，权重['wc3']，偏差['bc3']）
##最大池（下采样）
#conv3=tf.nn.local\u response\u normalization（conv3）
conv3=maxpool2d（conv3，k=2）
温度批次尺寸=tf.形状（x）[0]
以tf.name_scope（“deconv1”）作为作用域：
输出形状=[temp\u batch\u大小，50,50,64]
conv4=tf.nn.conv2d_转置（conv3，权重['wdc1']，输出_形状=输出_形状，步幅=[1,2,2,1]，padding=“有效”）
conv4=tf.nn.bias_add（conv4，bias['bdc1']）
conv4=tf.nn.relu（conv4）
#conv4=tf.nn.local\u response\u normalization（conv4）
以tf.name_scope（“deconv2”）作为作用域：
#输出形状=tf.pack（[temp\u batch\u size，100100,32]）
输出形状=[temp\u batch\u大小，100100,32]
conv5=tf.nn.conv2d_转置（conv4，权重['wdc2']，输出_形状=输出_形状，步幅=[1,2,2,1]，padding=“有效”）
conv5=tf.nn.bias_add（conv5，bias['bdc2']）
conv5=tf.nn.relu（co
import tensorflow as tf
import pdb
import numpy as np
from numpy import genfromtxt
from PIL import Image
# Parameters
learning_rate = 0.001
training_iters = 10000
batch_size = 10
display_step = 1
# Network Parameters
n_input = 200 # MNIST data input (img shape: 28*28)
n_output = 40000
n_classes = 2 # MNIST total classes (0-9 digits)
#n_input = 200
dropout = 0.75 # Dropout, probability to keep units
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input, n_input])
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
def convert_to_2_channel(x, batch_size):
#assume input has dimension (batch_size,x,y)
#output will have dimension (batch_size,x,y,2)
output = np.empty((batch_size, 200, 200, 2))
temp_arr1 = np.empty((batch_size, 200, 200))
temp_arr2 = np.empty((batch_size, 200, 200))
for i in xrange(batch_size):
for j in xrange(3):
for k in xrange(3):
if x[i][j][k] == 1:
temp_arr1[i][j][k] = 1
temp_arr2[i][j][k] = 0
else:
temp_arr1[i][j][k] = 0
temp_arr2[i][j][k] = 1
for i in xrange(batch_size):
for j in xrange(200):
for k in xrange(200):
for l in xrange(2):
if l == 0:
output[i][j][k][l] = temp_arr1[i][j][k]
else:
output[i][j][k][l] = temp_arr2[i][j][k]
return output
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 200, 200, 1])
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
#conv1 = tf.nn.local_response_normalization(conv1)
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
#conv2 = tf.nn.local_response_normalization(conv2)
conv2 = maxpool2d(conv2, k=2)
# Convolution Layer
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# # Max Pooling (down-sampling)
#conv3 = tf.nn.local_response_normalization(conv3)
conv3 = maxpool2d(conv3, k=2)
temp_batch_size = tf.shape(x)[0]
output_shape = [temp_batch_size, 50, 50, 64]
conv4 = tf.nn.conv2d_transpose(conv3, weights['wdc1'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv4 = tf.nn.bias_add(conv4, biases['bdc1'])
conv4 = tf.nn.relu(conv4)
# conv4 = tf.nn.local_response_normalization(conv4)
# output_shape = tf.pack([temp_batch_size, 100, 100, 32])
output_shape = [temp_batch_size, 100, 100, 32]
conv5 = tf.nn.conv2d_transpose(conv4, weights['wdc2'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv5 = tf.nn.bias_add(conv5, biases['bdc2'])
conv5 = tf.nn.relu(conv5)
# conv5 = tf.nn.local_response_normalization(conv5)
# output_shape = tf.pack([temp_batch_size, 200, 200, 1])
output_shape = [temp_batch_size, 200, 200, 2]
conv6 = tf.nn.conv2d_transpose(conv5, weights['wdc3'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv6 = tf.nn.bias_add(conv6, biases['bdc3'])
conv6 = tf.nn.relu(conv6)
# pdb.set_trace()
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv6, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
return (tf.add(tf.matmul(fc1, weights['out']), biases['out']))# Store layers weight & bias
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1' : tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2' : tf.Variable(tf.random_normal([5, 5, 32, 64])),
# 5x5 conv, 32 inputs, 64 outputs
'wc3' : tf.Variable(tf.random_normal([5, 5, 64, 128])),
'wdc1' : tf.Variable(tf.random_normal([2, 2, 64, 128])),
'wdc2' : tf.Variable(tf.random_normal([2, 2, 32, 64])),
'wdc3' : tf.Variable(tf.random_normal([2, 2, 2, 32])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([80000, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, 80000]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([128])),
'bdc1': tf.Variable(tf.random_normal([64])),
'bdc2': tf.Variable(tf.random_normal([32])),
'bdc3': tf.Variable(tf.random_normal([2])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([80000]))
}
# Construct model
pred = conv_net(x, weights, biases, keep_prob)
pred = tf.reshape(pred, [-1,n_input,n_input,n_classes])
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
# cost = (tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(0,tf.cast(tf.sub(tf.nn.sigmoid(pred),y), tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
summary = tf.train.SummaryWriter('/tmp/logdir/', sess.graph)
step = 1
from tensorflow.contrib.learn.python.learn.datasets.scroll import scroll_data
data = scroll_data.read_data('/home/kendall/Desktop/')
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = data.train.next_batch(batch_size)
# Run optimization op (backprop)
batch_x = batch_x.reshape((batch_size, n_input, n_input))
batch_y = batch_y.reshape((batch_size, n_input, n_input))
batch_y = convert_to_2_channel(batch_y, batch_size) #converts the 200x200 ground truth to a 200x200x2 classification
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
keep_prob: dropout})
#measure prediction
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: 1.})
print prediction
if step % display_step == 0:
# Calculate batch loss and accuracdef conv_net(x, weights, biases, dropout):
save_path = "model.ckpt"
saver.save(sess, save_path)
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: dropout})
print "Accuracy = " + str(acc)
if acc > 0.73:
break
step += 1
print "Optimization Finished!"
#make prediction
im = Image.open('/home/kendall/Desktop/HA900_frames/frame0035.tif')
batch_x = np.array(im)
# pdb.set_trace()
batch_x = batch_x.reshape((1, n_input, n_input))
batch_x = batch_x.astype(float)
pdb.set_trace()
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: dropout})
print prediction
arr1 = np.empty((n_input,n_input))
arr2 = np.empty((n_input,n_input))
for i in xrange(n_input):
for j in xrange(n_input):
for k in xrange(2):
if k == 0:
arr1[i][j] = (prediction[0][i][j][k])
else:
arr2[i][j] = (prediction[0][i][j][k])
# prediction = np.asarray(prediction)
# prediction = np.reshape(prediction, (200,200))
# np.savetxt("prediction.csv", prediction, delimiter=",")
np.savetxt("prediction1.csv", arr1, delimiter=",")
np.savetxt("prediction2.csv", arr2, delimiter=",")
# np.savetxt("prediction2.csv", arr2, delimiter=",")
# Calculate accuracy for 256 mnist test images
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: data.test.images[:256],
y: data.test.labels[:256],
keep_prob: 1.})
with tf.name_scope("loss") as scope:
# cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
temp_pred = tf.reshape(pred, [-1, 2])
temp_y = tf.reshape(y, [-1, 2])
cost = (tf.nn.softmax_cross_entropy_with_logits(temp_pred, temp_y))
import tensorflow as tf
import pdb
import numpy as np
from numpy import genfromtxt
from PIL import Image
# Parameters
learning_rate = 0.001
training_iters = 10000
batch_size = 10
display_step = 1
# Network Parameters
n_input = 200 # MNIST data input (img shape: 28*28)
n_output = 40000
n_classes = 2 # MNIST total classes (0-9 digits)
#n_input = 200
dropout = 0.75 # Dropout, probability to keep units
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input, n_input])
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
def convert_to_2_channel(x, batch_size):
#assume input has dimension (batch_size,x,y)
#output will have dimension (batch_size,x,y,2)
output = np.empty((batch_size, 200, 200, 2))
temp_arr1 = np.empty((batch_size, 200, 200))
temp_arr2 = np.empty((batch_size, 200, 200))
for i in xrange(batch_size):
for j in xrange(3):
for k in xrange(3):
if x[i][j][k] == 1:
temp_arr1[i][j][k] = 1
temp_arr2[i][j][k] = 0
else:
temp_arr1[i][j][k] = 0
temp_arr2[i][j][k] = 1
for i in xrange(batch_size):
for j in xrange(200):
for k in xrange(200):
for l in xrange(2):
if l == 0:
output[i][j][k][l] = temp_arr1[i][j][k]
else:
output[i][j][k][l] = temp_arr2[i][j][k]
return output
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 200, 200, 1])
with tf.name_scope("conv1") as scope:
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
#conv1 = tf.nn.local_response_normalization(conv1)
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
with tf.name_scope("conv2") as scope:
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
#conv2 = tf.nn.local_response_normalization(conv2)
conv2 = maxpool2d(conv2, k=2)
# Convolution Layer
with tf.name_scope("conv3") as scope:
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# # Max Pooling (down-sampling)
#conv3 = tf.nn.local_response_normalization(conv3)
conv3 = maxpool2d(conv3, k=2)
temp_batch_size = tf.shape(x)[0]
with tf.name_scope("deconv1") as scope:
output_shape = [temp_batch_size, 50, 50, 64]
conv4 = tf.nn.conv2d_transpose(conv3, weights['wdc1'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv4 = tf.nn.bias_add(conv4, biases['bdc1'])
conv4 = tf.nn.relu(conv4)
# conv4 = tf.nn.local_response_normalization(conv4)
with tf.name_scope("deconv2") as scope:
# output_shape = tf.pack([temp_batch_size, 100, 100, 32])
output_shape = [temp_batch_size, 100, 100, 32]
conv5 = tf.nn.conv2d_transpose(conv4, weights['wdc2'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv5 = tf.nn.bias_add(conv5, biases['bdc2'])
conv5 = tf.nn.relu(conv5)
# conv5 = tf.nn.local_response_normalization(conv5)
with tf.name_scope("deconv3") as scope:
# output_shape = tf.pack([temp_batch_size, 200, 200, 1])
output_shape = [temp_batch_size, 200, 200, 2]
conv6 = tf.nn.conv2d_transpose(conv5, weights['wdc3'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv6 = tf.nn.bias_add(conv6, biases['bdc3'])
# conv6 = tf.nn.relu(conv6)
# pdb.set_trace()
conv6 = tf.nn.dropout(conv6, dropout)
return conv6
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
# fc1 = tf.reshape(conv6, [-1, weights['wd1'].get_shape().as_list()[0]])
# fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
# fc1 = tf.nn.relu(fc1)
# # Apply Dropout
# fc1 = tf.nn.dropout(fc1, dropout)
#
# return (tf.add(tf.matmul(fc1, weights['out']), biases['out']))# Store layers weight & bias
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1' : tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2' : tf.Variable(tf.random_normal([5, 5, 32, 64])),
# 5x5 conv, 32 inputs, 64 outputs
'wc3' : tf.Variable(tf.random_normal([5, 5, 64, 128])),
'wdc1' : tf.Variable(tf.random_normal([2, 2, 64, 128])),
'wdc2' : tf.Variable(tf.random_normal([2, 2, 32, 64])),
'wdc3' : tf.Variable(tf.random_normal([2, 2, 2, 32])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([80000, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, 80000]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([128])),
'bdc1': tf.Variable(tf.random_normal([64])),
'bdc2': tf.Variable(tf.random_normal([32])),
'bdc3': tf.Variable(tf.random_normal([2])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([80000]))
}
# Construct model
# with tf.name_scope("net") as scope:
pred = conv_net(x, weights, biases, keep_prob)
pred = tf.reshape(pred, [-1,n_input,n_input,n_classes])
# Define loss and optimizer
with tf.name_scope("loss") as scope:
# cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
temp_pred = tf.reshape(pred, [-1, 2])
temp_y = tf.reshape(y, [-1, 2])
cost = (tf.nn.softmax_cross_entropy_with_logits(temp_pred, temp_y))
with tf.name_scope("opt") as scope:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
with tf.name_scope("acc") as scope:
correct_pred = tf.equal(0,tf.cast(tf.sub(tf.nn.softmax(temp_pred),y), tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
summary = tf.train.SummaryWriter('/tmp/logdir/', sess.graph)
step = 1
from tensorflow.contrib.learn.python.learn.datasets.scroll import scroll_data
data = scroll_data.read_data('/home/kendall/Desktop/')
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = data.train.next_batch(batch_size)
# Run optimization op (backprop)
batch_x = batch_x.reshape((batch_size, n_input, n_input))
batch_y = batch_y.reshape((batch_size, n_input, n_input))
batch_y = convert_to_2_channel(batch_y, batch_size) #converts the 200x200 ground truth to a 200x200x2 classification
batch_y = batch_y.reshape(batch_size * n_input * n_input, 2)
sess.run(optimizer, feed_dict={x: batch_x, temp_y: batch_y,
keep_prob: dropout})
#measure prediction
prediction = sess.run(tf.nn.softmax(temp_pred), feed_dict={x: batch_x, keep_prob: dropout})
print prediction
if step % display_step == 0:
# Calculate batch loss and accuracdef conv_net(x, weights, biases, dropout):
save_path = "model.ckpt"
saver.save(sess, save_path)
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: dropout})
print "Accuracy = " + str(acc)
if acc > 0.73:
break
step += 1
print "Optimization Finished!"
#make prediction
im = Image.open('/home/kendall/Desktop/HA900_frames/frame0035.tif')
batch_x = np.array(im)
# pdb.set_trace()
batch_x = batch_x.reshape((1, n_input, n_input))
batch_x = batch_x.astype(float)
pdb.set_trace()
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: dropout})
print prediction
arr1 = np.empty((n_input,n_input))
arr2 = np.empty((n_input,n_input))
for i in xrange(n_input):
for j in xrange(n_input):
for k in xrange(2):
if k == 0:
arr1[i][j] = (prediction[0][i][j][k])
else:
arr2[i][j] = (prediction[0][i][j][k])
# prediction = np.asarray(prediction)
# prediction = np.reshape(prediction, (200,200))
# np.savetxt("prediction.csv", prediction, delimiter=",")
np.savetxt("prediction1.csv", arr1, delimiter=",")
np.savetxt("prediction2.csv", arr2, delimiter=",")
# np.savetxt("prediction2.csv", arr2, delimiter=",")
# Calculate accuracy for 256 mnist test images
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: data.test.images[:256],
y: data.test.labels[:256],
keep_prob: 1.})
conv6 = tf.nn.bias_add(conv6, biases['bdc3'])
for j in xrange(3):