y_test[i + 11439, :] = newlandmarks
## F1
x = tf.placeholder(tf.float32, shape=[None, 39, 39],
name='x') #input imagematrix_data to be fed
y = tf.placeholder(tf.float32, shape=[None, 10],
name='y') #correct output to be fed
keep_prob = tf.placeholder(tf.float32,
name='keep_prob') #keep_prob parameter to be fed
x_image = tf.reshape(x, [-1, 39, 39, 1])
## convolutional layer 1, kernel 4*4, insize 1, outsize 20
W_conv1 = nl.weight_variable([4, 4, 1, 20])
b_conv1 = nl.bias_variable([20])
h_conv1 = nl.conv_layer(x_image, W_conv1) + b_conv1 #outsize = batch*36*36*20
a_conv1 = tf.nn.relu(h_conv1) #outsize = batch*36*36*20
## max pooling layer 1
h_pool1 = nl.max_pool_22_layer(a_conv1) #outsize = batch*18*18*20
a_pool1 = tf.nn.relu(h_pool1) #outsize = batch*18*18*20
## convolutional layer 2, kernel 3*3, insize 20, outsize 40
W_conv2 = nl.weight_variable([3, 3, 20, 40])
b_conv2 = nl.bias_variable([40])
h_conv2 = nl.conv_layer(a_pool1, W_conv2) + b_conv2 #outsize = batch*16*16*40
a_conv2 = tf.nn.relu(h_conv2) #outsize = batch*16*16*40
## max pooling layer 2
h_pool2 = nl.max_pool_22_layer(a_conv2) #outsize = batch*8*8*40
a_pool2 = tf.nn.relu(h_pool2) #outsize = batch*8*8*40
newlandmarks[1] = (1-ry) / 2 * 39 #ratio y
#one dimension which represents one grey picture, set the first dimension as index
x_test[14390+i*10+j,:,:] = imagematrix
y_test[14390+i*10+j,:] = newlandmarks
## RE31
x = tf.placeholder(tf.float32, shape=[None,15,15], name='x') #input imagematrix_data to be fed
y = tf.placeholder(tf.float32, shape=[None,2], name='y') #correct output to be fed
keep_prob = tf.placeholder(tf.float32, name='keep_prob') #keep_prob parameter to be fed
x_image = tf.reshape(x, [-1,15,15,1])
## convolutional layer 1, kernel 4*4, insize 1, outsize 20
W_conv1 = nl.weight_variable([4,4,1,20])
b_conv1 = nl.bias_variable([20])
h_conv1 = nl.conv_layer(x_image, W_conv1) + b_conv1 #outsize = batch*12*12*20
a_conv1 = tf.nn.tanh(h_conv1) #outsize = batch*12*12*20
## max pooling layer 1
h_pool1 = nl.max_pool_22_layer(a_conv1) #outsize = batch*6*6*20
a_pool1 = tf.nn.tanh(h_pool1) #outsize = batch*6*6*20
## convolutional layer 2, kernel 3*3, insize 20, outsize 40
W_conv2 = nl.weight_variable([3,3,20,40])
b_conv2 = nl.bias_variable([40])
h_conv2 = nl.conv_layer(a_pool1, W_conv2) + b_conv2 #outsize = batch*4*4*40
a_conv2 = tf.nn.tanh(h_conv2) #outsize = batch*4*4*40
## max pooling layer 2
h_pool2 = nl.max_pool_22_layer(a_conv2) #outsize = batch*2*2*40
a_pool2 = tf.nn.tanh(h_pool2) #outsize = batch*2*2*40
newlandmarks[k] = (rawlandmarks[k].value - test_table.cell(i+1440,1).value + 0.05 * height) / (1.1 * height) * 39
#one dimension which represents one grey picture, set the first dimension as index
x_test[i+1439,:,:] = imagematrix
y_test[i+1439,:] = newlandmarks
## F1
x = tf.placeholder(tf.float32, shape=[None,39,39], name='x') #input imagematrix_data to be fed
y = tf.placeholder(tf.float32, shape=[None,10], name='y') #correct output to be fed
keep_prob = tf.placeholder(tf.float32, name='keep_prob') #keep_prob parameter to be fed
x_image = tf.reshape(x, [-1,39,39,1])
## convolutional layer 1, kernel 4*4, insize 1, outsize 20
W_conv1 = tf.Variable(tf.truncated_normal(shape=[4,4,1,20], stddev=0.1), name = 'W_conv1')
b_conv1 = tf.Variable(tf.constant(0.1, shape=[20]), name = 'b_conv1')
h_conv1 = nl.conv_layer(x_image, W_conv1) + b_conv1 #outsize = batch*36*36*20
a_conv1 = tf.nn.relu(h_conv1) #outsize = batch*36*36*20
## max pooling layer 1
h_pool1 = nl.max_pool_22_layer(a_conv1) #outsize = batch*18*18*20
a_pool1 = tf.nn.relu(h_pool1) #outsize = batch*18*18*20
## flatten layer
x_flat = tf.reshape(a_pool1, [-1,6480]) #outsize = batch*6480
## fully connected layer 1
W_fc1 = tf.Variable(tf.truncated_normal(shape=[6480,120], stddev=0.1), name = 'W_fc1')
b_fc1 = tf.Variable(tf.constant(0.1, shape=[120]), name = 'b_fc1')
h_fc1 = tf.matmul(x_flat, W_fc1) + b_fc1 #outsize = batch*120
a_fc1 = tf.nn.relu(h_fc1) #outsize = batch*120
a_fc1_dropout = tf.nn.dropout(a_fc1, keep_prob) #dropout layer 1
