## 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 ## flatten layer x_flat = tf.reshape(a_pool2, [-1,160]) #outsize = batch*160
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
## convolutional layer 3, kernel 3*3, insize 40, outsize 60
W_conv3 = nl.weight_variable([3, 3, 40, 60])
b_conv3 = nl.bias_variable([60])
EN1_b_conv2 = bias_variable([40]) EN1_h_conv2 = conv2d(EN1_a_pool1, EN1_W_conv2) + EN1_b_conv2 #outsize = batch*12*16*40 EN1_a_conv2 = tf.nn.tanh(EN1_h_conv2) #outsize = batch*12*16*40 ## max pooling layer 2 EN1_h_pool2 = max_pool_22(EN1_a_conv2) #outsize = batch*6*8*40 EN1_a_pool2 = tf.nn.tanh(EN1_h_pool2) #outsize = batch*6*8*40 ## convolutional layer 3, kernel 3*3, insize 40, outsize 60 EN1_W_conv3 = weight_variable([3,3,40,60]) EN1_b_conv3 = bias_variable([60]) EN1_h_conv3 = conv2d(EN1_a_pool2, EN1_W_conv3) + EN1_b_conv3 #outsize = batch*4*6*60 EN1_a_conv3 = tf.nn.tanh(EN1_h_conv3) #outsize = batch*4*6*60 ## max pooling layer 3 EN1_h_pool3 = nl.max_pool_22_layer(EN1_a_conv3) #outsize = batch*2*3*60 EN1_a_pool3 = tf.nn.tanh(EN1_h_pool3) #outsize = batch*2*3*60 ## convolutional layer 4, kernel 2*2, insize 60, outsize 80 EN1_W_conv4 = weight_variable([2,2,60,80]) EN1_b_conv4 = bias_variable([80]) EN1_h_conv4 = conv2d(EN1_a_pool3, EN1_W_conv4) + EN1_b_conv4 #outsize = batch*1*2*80 EN1_a_conv4 = tf.nn.tanh(EN1_h_conv4) #outsize = batch*1*2*80 ## flatten layer EN1_x_flat = tf.reshape(EN1_a_conv4, [-1,160]) #outsize = batch*160 ## fully connected layer 1 EN1_W_fc1 = weight_variable([160,100]) EN1_b_fc1 = bias_variable([100]) EN1_h_fc1 = tf.matmul(EN1_x_flat, EN1_W_fc1) + EN1_b_fc1 #outsize = batch*100
## 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 ## fully connected layer 2 W_fc2 = tf.Variable(tf.truncated_normal(shape=[120,10], stddev=0.1), name = 'W_fc2') b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]), name = 'b_fc2')