def code_classifier_forward(config,
incoming=None,
image=None,
scope="code_classifier",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
output = relu(fully_connected(incoming, 512))
output1 = dropout(output, 0.8)
print(config.batch_size, image.shape)
output = relu(
fully_connected(
tf.reshape(image, [config.batch_size, 32 * 32 * 3]), 512))
output2 = dropout(output, 0.8)
output = tf.concat([output1, output2], axis=-1)
output = relu(fully_connected(output, 1024))
output = dropout(output, 0.5)
output = relu(fully_connected(output, 512))
output = dropout(output, 0.8)
output = fully_connected(output, 10)
return output
def residual_block(self,
input_,
n_filters,
filter_size,
n_blocks,
stride=1):
strides = stride
for block in range(n_blocks):
n, h, w, c = input_.get_shape().as_list()
if block > 0:
strides = 1
conv1 = conv_2d(input_,
n_filters,
filter_size,
strides=strides,
activation='linear')
act1 = relu(bn(conv1))
conv2 = conv_2d(act1,
n_filters,
filter_size,
strides=1,
activation='linear')
act2 = bn(conv2)
if stride != 1 and block == 0:
input_ = max_pool_2d(input_, [1, 3], [1, stride])
if n_filters != c:
input_ = conv_2d(input_, n_filters, 1, activation='linear')
input_ = relu(input_ + act2)
return input_
def code_classifier_forward(config, incoming=None, image=None,
scope="code_classifier", name=None, reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
code_output = leaky_relu(fully_connected(incoming, 512))
output = leaky_relu(fully_connected(tf.reshape(image, [config.batch_size, 28 * 28]), 512))
prod = tf.matmul(code_output[:, :, None], output[:, None, :])
prob = tf.nn.softmax(prod)
prob2 = tf.nn.softmax(tf.transpose(prod, perm=[0, 2, 1]))
output = tf.concat([code_output,
tf.matmul(prob, output[:, :, None])[:, :, 0],
tf.matmul(prob2, code_output[:, :, None])[:, :, 0]], axis=-1)
output = relu(fully_connected(output, 1024))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 512))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 256))
output = dropout(output, 0.8)
output = fully_connected(output, 10)
return output
def classifier_forward(config,
incoming,
name=None,
reuse=False,
scope="classifier"):
with tf.variable_scope(scope, name, reuse=reuse):
network = incoming
network = relu(
batch_normalization(
conv_2d(network,
32,
5,
activation='relu',
regularizer="L2",
strides=2)))
network = relu(
batch_normalization(
conv_2d(network,
64,
5,
activation='relu',
regularizer="L2",
strides=2)))
network = flatten(network)
network = relu(batch_normalization(fully_connected(network, 1024)))
network = dropout(network, 0.5)
network = fully_connected(network, 10)
return network
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv_1_0 = relu(
batch_normalization(
conv_2d(net,
128,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(
batch_normalization(
conv_2d(tower_conv_1_0,
160, [1, 7],
bias=False,
activation=None,
name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(
batch_normalization(
conv_2d(tower_conv_1_1,
192, [7, 1],
bias=False,
activation=None,
name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv, tower_conv_1_2],
mode='concat',
axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def generator_forward(config,
noise=None,
scope="generator",
name=None,
reuse=False,
num_samples=-1):
with tf.variable_scope(scope, name, reuse=reuse):
if noise is None:
noise = tf.random_normal(
[config.batch_size if num_samples == -1 else num_samples, 128],
name="noise")
output = fully_connected(noise,
4 * 4 * 8 * config.gen_dim,
name="input")
output = tf.reshape(output, [-1, 4, 4, 8 * config.gen_dim])
output = batch_normalization(output)
output = relu(output)
output = conv_2d_transpose(output,
4 * config.gen_dim,
5, [8, 8],
name="conv1",
strides=2)
output = batch_normalization(output)
output = relu(output)
output = conv_2d_transpose(output,
2 * config.gen_dim,
5, [16, 16],
name="conv2",
strides=2)
output = batch_normalization(output)
output = relu(output)
output = conv_2d_transpose(output,
config.gen_dim,
5, [32, 32],
name="conv3",
strides=2)
output = batch_normalization(output)
output = relu(output)
output = conv_2d_transpose(output,
3,
5, [64, 64],
name="conv4",
strides=2)
output = tf.tanh(output)
return output
def resLayer(x, filters, stride=1):
network = conv_2d(x, filters, 3, activation=None, strides=stride)
network = batch_normalization(network)
network = relu(network)
network = conv_2d(network, filters, 3, activation=None)
network = batch_normalization(network)
if stride != 1:
x = max_pool_2d(x, 2)
x = conv_2d(x, filters, 1)
network = x + network
network = relu(network)
return network
def code_classifier_forward(config,
incoming=None,
image=None,
scope="code_classifier",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
code_output = leaky_relu(fully_connected(incoming, 512))
output = conv_2d(image, 32, 5, 2, name="conv1")
output = residual_block(output,
2,
32,
downsample=True,
batch_norm=True,
name="rb1")
output = residual_block(output,
1,
64,
downsample=True,
batch_norm=True,
name="rb2")
output = leaky_relu(
fully_connected(
tf.reshape(output, [config.batch_size, 4 * 4 * 64]), 1024))
prod = tf.matmul(code_output[:, :, None], output[:, None, :])
prob = tf.nn.softmax(prod)
prob2 = tf.nn.softmax(tf.transpose(prod, perm=[0, 2, 1]))
output = tf.concat([
code_output,
tf.matmul(prob, output[:, :, None])[:, :, 0],
tf.matmul(prob2, code_output[:, :, None])[:, :, 0]
],
axis=-1)
output = relu(fully_connected(output, 1024))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 512))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 256))
output = dropout(output, 0.8)
output = fully_connected(output, 5)
return output
def alchNetEnhance(img_prep, img_aug, learning_rate):
network = input_data(shape=[None, 64, 64, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 64, 3, activation=None)
network = batch_normalization(network)
network = relu(network)
network = resLayer(network, 64)
network = resLayer(network, 64)
network = resLayer(network, 128, stride=2)
network = resLayer(network, 128)
network = resLayer(network, 256, stride=2)
network = resLayer(network, 256)
network = resLayer(network, 512, stride=2)
network = resLayer(network, 512)
network = fully_connected(network, 1024, activation='relu')
network = batch_normalization(network,
stddev=0.002,
trainable=True,
restore=True,
reuse=False)
network = dropout(network, 0.5)
network = fully_connected(network, 200, activation='softmax')
network = regression(network,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=learning_rate)
return network
def residual_block_1D(incoming,out_channels,downsample=False, first=False, filt_len=16, dropout_prob=0.85, downsampleSecond=True): resnet = incoming in_channels = incoming.shape[-1].value strides = (2 if downsample else 1) dsLayer = (1 if downsampleSecond else 0) identity = resnet nConv = 2 if first: resnet = conv_1d(resnet, out_channels, filt_len, strides,weights_init="variance_scaling") nConv = 1 for i in range(nConv): resnet = batch_normalization(resnet) resnet = relu(resnet) resnet = dropout(resnet, dropout_prob) if downsample and i==dsLayer: #1 as in, second layer resnet = conv_1d(resnet,out_channels,filt_len, strides=1, weights_init="variance_scaling") #puts the downsampling on the first conv layer only else: resnet = conv_1d(resnet,out_channels,filt_len, strides, weights_init="variance_scaling") #Beginning of skip connection identity = max_pool_1d(identity,strides, strides) if in_channels != out_channels: ch = (out_channels - in_channels) // 2 identity = tf.pad(identity,[[0,0],[0,0],[ch,ch]]) in_channels = out_channels resnet = resnet + identity return resnet
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def classifier_forward(config,
incoming,
name=None,
reuse=False,
scope="classifier"):
with tf.variable_scope(scope, name, reuse=reuse):
network = incoming
network = relu(conv_2d(network, 32, 5, strides=2))
network = relu(conv_2d(network, 64, 5, strides=2))
network = flatten(network)
network = relu(fully_connected(network, 1024))
network = dropout(network, 0.7)
network = fully_connected(network, 10)
return network
def res18_forward(incoming, scope=None, name="resnet_18", reuse=False):
with tf.variable_scope(scope, default_name=name, reuse=reuse):
network = conv_2d(incoming, 32, 5, 2, name="conv1",)
network = residual_block(network, 2, 32, downsample=True, batch_norm=True, name="rb1")
network = residual_block(network, 2, 64, downsample=True, batch_norm=True, name="rb2")
network = residual_block(network, 2, 128, downsample=True, batch_norm=True, name="rb3")
network = residual_block(network, 2, 256, downsample=True, batch_norm=True, name="rb4")
network = relu(batch_normalization(fully_connected(network, 256, name="fc1")))
network = fully_connected(network, 5, name="fc2")
return network
def ProteinNet(str_len, emb, lr, num_classes):
in_layer = input_data([None, 1, str_len * 2 + 2, 1])
indices = in_layer[:, 0, :2, 0]
if emb > 1:
lstm1 = lstm(embedding(in_layer[:, 0, 2:, 0], 26, emb),
300,
return_seq=True)
else:
lstm1 = lstm(in_layer[:, 0, 2:, :], 300, return_seq=True)
# lstm branch
lstm2 = lstm(lstm1, 300, return_seq=True)
lstm3 = lstm(lstm2, 300, return_seq=True)
lstm4 = lstm(lstm3, 300)
# cnn branch
in_layer = bn(in_layer)
conv1 = conv_2d(in_layer, 64, [1, 10], 1)
norm1 = relu(bn(conv1))
conv2 = conv_2d(norm1, 128, [1, 6], 2)
norm2 = relu(bn(conv2))
conv3 = conv_2d(norm2, 256, [1, 3], 2)
norm3 = relu(bn(conv3))
gap = tf.reshape(global_avg_pool(norm3), [-1, 256])
# fully-connected branch
fc_ind = fc(indices, 50, activation='tanh')
fc_ind2 = fc(fc_ind, 50, activation='tanh')
# merge lstm, conv, and fc layers
merged = tf.concat([lstm4, gap, fc_ind2], 1)
out = fc(merged, num_classes, activation='softmax')
net = regression(out,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=lr)
model = tflearn.DNN(net, tensorboard_verbose=2, tensorboard_dir='.')
return model
def aprelu(incoming):
in_channels = incoming.get_shape().as_list()[-1]
scales_n = tf.reduce_mean(tf.reduce_mean(tf.minimum(incoming,0),axis=2,keep_dims=True),axis=1,keep_dims=True)
scales_p = tf.reduce_mean(tf.reduce_mean(tf.maximum(incoming,0),axis=2,keep_dims=True),axis=1,keep_dims=True)
scales = tf.concat([scales_n, scales_p],axis=3)
scales = fully_connected(scales, in_channels, activation='linear',regularizer='L2',
weight_decay=0.0001,weights_init='variance_scaling')
scales = relu(bn(scales))
scales = fully_connected(scales, in_channels, activation='linear',regularizer='L2',
weight_decay=0.0001,weights_init='variance_scaling')
scales = tflearn.activations.sigmoid(bn(scales))
scales = tf.expand_dims(tf.expand_dims(scales,axis=1),axis=1)
return tf.maximum(incoming, 0) + tf.multiply(scales, (incoming - tf.abs(incoming))) * 0.5
def fcLayers(inputs, c):
'''
Fully connected layers that can be used for feature extraction
:param vector: inputs
:param dict: config dictionary c
:return: Add fully connected features layers to tf graph
'''
inputs = tf.layers.flatten(inputs)
if c.fcfe.normalise:
inputs = tf.div(tf.subtract(inputs,tf.reduce_min(inputs)),\
tf.subtract(tf.reduce_max(inputs), tf.reduce_min(inputs)))
for s in c.fcfe.layers:
inputs = relu(tflearn.fully_connected(inputs, s, weights_init=c.fcfe.w_init))
return inputs
def generator_forward(config,
labels,
noise=None,
scope="generator",
name=None,
reuse=False,
num_samples=-1):
with tf.variable_scope(scope, name, reuse=reuse):
if noise is None:
noise = tf.random_normal(
[config.batch_size if num_samples == -1 else num_samples, 128],
name="noise")
embed = fully_connected(labels, 8 * config.dim)
noise = fully_connected(noise, 56 * config.dim)
cat = relu(batch_normalization(tf.concat([embed, noise], axis=-1)))
output = fully_connected(cat, 4 * 4 * 4 * config.dim)
output = batch_normalization(output)
output = tf.nn.relu(output)
output = tf.reshape(output, [-1, 4, 4, 4 * config.dim])
output = conv_2d_transpose(output,
2 * config.dim,
5, [8, 8],
strides=2)
output = output[:, :7, :7, :]
output = batch_normalization(output)
output = relu(output)
output = conv_2d_transpose(output, config.dim, 5, [14, 14], strides=2)
output = batch_normalization(output)
output = tf.nn.relu(output)
output = conv_2d_transpose(output, 1, 5, [28, 28], strides=2)
output = tf.tanh(output)
return output
def network(self):
in_layer = input_data([None, 1, self.str_len * 2 + 2, 1])
indices = in_layer[:, 0, :2, 0]
if self.emb > 1:
lstm1 = lstm(embedding(in_layer[:, 0, 2:, 0], 26, self.emb),
300,
return_seq=True)
else:
lstm1 = lstm(in_layer[:, 0, 2:, :], 300, return_seq=True)
# lstm branch
lstm2 = lstm(lstm1, 300, return_seq=True)
lstm3 = lstm(lstm2, 300, return_seq=True)
lstm4 = lstm(lstm3, 300)
# cnn branch
in_layer = bn(in_layer)
conv1 = conv_2d(in_layer, 64, [1, 7], 1)
norm1 = relu(bn(conv1))
block1 = self.residual_block(norm1, 128, [1, 3], 2, stride=2)
block2 = self.residual_block(block1, 256, [1, 3], 2, stride=2)
block3 = self.residual_block(block2, 512, [1, 3], 2)
block4 = self.residual_block(block3, 1024, [1, 3], 2)
n_out_filters = block4.get_shape().as_list()[-1]
gap = tf.reshape(global_avg_pool(block4), [-1, n_out_filters])
# fully-connected branch
fc_ind = fc(indices, 100, activation='tanh')
fc_ind2 = fc(fc_ind, 100, activation='tanh')
# merge lstm, conv, and fc layers
merged = tf.concat([lstm4, gap, fc_ind2], 1)
out = fc(merged, self.num_classes,
activation='softmax') # output layer
# describe optimization
net = regression(out,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=self.lr)
# build model
model = tflearn.DNN(net, tensorboard_verbose=2, tensorboard_dir='.')
return model
def generator_forward(config, noise=None, scope="generator", name=None, reuse=False, num_samples=-1):
with tf.variable_scope(scope, name, reuse=reuse):
if noise is None:
noise = tf.random_normal([config.batch_size if num_samples == -1 else num_samples, 128], name="noise")
output = fully_connected(noise, 6 * 6 * 8 * config.gen_dim, name="input")
output = tf.reshape(output, [-1, 6, 6, 8 * config.gen_dim])
output = residual_block_upsample(output, 8 * config.gen_dim, 5, name="conv1")
output = residual_block_upsample(output, 4 * config.gen_dim, 5, name="conv2")
output = residual_block_upsample(output, 2 * config.gen_dim, 5, name="conv3")
output = batch_normalization(output)
output = relu(output)
output = conv_2d(output, 3, 3, name="conv4")
output = tf.tanh(output)
return output
def convLayers(inputs, c):
'''
Conv layers that can be used for feature extraction
:param vector: inputs
:param dict: config dictionary c
:return: Add conv features layers to tf graph
'''
weights_init = tflearn.initializations.xavier()
bias_init = tf.constant_initializer(0.1)
#print(inputs.get_shape())
inputs = tf.div(inputs, c.cnn.max_in)
layer = 0
for (o, k, s) in zip(c.cnn.outdim, c.cnn.kernels, c.cnn.stride):
inputs = conv2d(inputs, o, [k, k], [s, s], name='conv' + str(layer), format=c.cnn.format)
layer += 1
shape = inputs.get_shape().as_list()
inputs = tf.reshape(inputs, [-1, reduce(lambda x, y: x * y, shape[1:])])
return relu(tflearn.fully_connected(inputs, c.cnn.fc, weights_init=weights_init,bias_init=bias_init))
def resNet(img_prep, img_aug, learning_rate):
network = input_data(shape=[None, 64, 64, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 64, 3, regularizer='L2', weight_decay=0.0001)
network = residual_block(network, 2, 64)
network = residual_block(network, 1, 128, downsample=True)
network = residual_block(network, 1, 128)
network = residual_block(network, 1, 256, downsample=True)
network = residual_block(network, 1, 256)
network = residual_block(network, 1, 512, downsample=True)
network = residual_block(network, 1, 512)
network = batch_normalization(network)
network = relu(network)
network = global_avg_pool(network)
network = fully_connected(network, 200, activation='softmax')
network = regression(network,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=learning_rate)
return network
def ImageNetInceptionV2(outnode,
model_name,
target,
opt,
learn_r,
epch,
dropout_keep_rate,
save_model=False):
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
32,
3,
bias=False,
activation=None,
name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
48,
3,
bias=False,
activation=None,
name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
64,
3,
bias=False,
activation=None,
name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2],
mode='concat',
axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv_1_0 = relu(
batch_normalization(
conv_2d(net,
128,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(
batch_normalization(
conv_2d(tower_conv_1_0,
160, [1, 7],
bias=False,
activation=None,
name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(
batch_normalization(
conv_2d(tower_conv_1_1,
192, [7, 1],
bias=False,
activation=None,
name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv, tower_conv_1_2],
mode='concat',
axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
224, [1, 3],
bias=False,
activation=None,
name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
256, [3, 1],
bias=False,
name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv, tower_conv1_2], mode='concat', axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
# default = 0.8
dropout_keep_prob = dropout_keep_rate
network = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
conv1a_3_3 = relu(
batch_normalization(
conv_2d(network,
32,
3,
strides=2,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(
batch_normalization(
conv_2d(conv1a_3_3,
32,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(
batch_normalization(
conv_2d(conv2a_3_3,
64,
3,
bias=False,
activation=None,
name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_3a_3x3')
conv3b_1_1 = relu(
batch_normalization(
conv_2d(maxpool3a_3_3,
80,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(
batch_normalization(
conv_2d(conv3b_1_1,
192,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_5a_3x3')
tower_conv = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
96,
1,
bias=False,
activation=None,
name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
48,
1,
bias=False,
activation=None,
name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
64,
5,
bias=False,
activation=None,
name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3,
3,
strides=1,
padding='same',
name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(
batch_normalization(
conv_2d(tower_pool3_0,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge(
[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1],
mode='concat',
axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
tower_conv = relu(
batch_normalization(
conv_2d(net,
384,
3,
bias=False,
strides=2,
activation=None,
padding='VALID',
name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
256,
3,
bias=False,
activation=None,
name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(
batch_normalization(
conv_2d(tower_conv,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1,
288,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='COnv2d_1a_3x3')))
tower_conv2 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2,
288,
3,
bias=False,
name='Conv2d_0b_3x3',
activation=None)))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
320,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool],
mode='concat',
axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(
batch_normalization(
conv_2d(net,
1536,
1,
bias=False,
activation=None,
name='Conv2d_7b_1x1')))
net = avg_pool_2d(net,
net.get_shape().as_list()[1:3],
strides=2,
padding='VALID',
name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, outnode, activation='softmax')
str_model_name = "{}_{}_{}_{}_{}_{}".format(model_name, target, opt,
learn_r, epch,
dropout_keep_rate)
network = tflearn.regression(loss,
optimizer=opt,
loss='categorical_crossentropy',
learning_rate=learn_r,
name='targets')
model = None
if save_model:
model = tflearn.DNN(
network,
checkpoint_path='../tflearnModels/{}'.format(str_model_name),
best_checkpoint_path='../tflearnModels/bestModels/best_{}'.format(
str_model_name),
max_checkpoints=1,
tensorboard_verbose=0,
tensorboard_dir="../tflearnLogs/{}/".format(str_model_name))
else:
model = tflearn.DNN(network)
return model
def _build_model(self):
# features
# 随机生成embedding
self.drug_embedding = weight_variable([num_drug, dim_drug])
self.protein_embedding = weight_variable([num_protein, dim_protein])
self.disease_embedding = weight_variable([num_disease, dim_disease])
self.sideeffect_embedding = weight_variable(
[num_sideeffect, dim_sideeffect])
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.drug_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.protein_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.disease_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.sideeffect_embedding))
# feature passing weights (maybe different types of nodes can use different weights)
W0 = weight_variable([dim_pass + dim_drug, dim_drug])
b0 = bias_variable([dim_drug])
tf.add_to_collection('l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(W0))
# passing 1 times (can be easily extended to multiple passes)
drug_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.drug_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_chemical_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.drug_sideeffect_normalize, a_layer(self.sideeffect_embedding, dim_pass)) + \
tf.matmul(self.drug_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.drug_embedding], axis=1), W0) + b0), dim=1)
drug_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.drug_drug_normalize, a_layer(self.drug_embedding, dim_pass)) * 4, \
self.drug_embedding], axis=1), W0) + b0), dim= 1)
protein_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.protein_protein_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_sequence_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.protein_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.protein_embedding], axis=1), W0) + b0), dim=1)
# 改
protein_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.protein_protein_normalize, a_layer(self.protein_embedding, dim_pass)) * 4, \
self.protein_embedding], axis=1), W0) + b0), dim=1)
disease_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.disease_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.disease_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.disease_embedding], axis=1), W0) + b0), dim=1)
disease_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.disease_drug_normalize, a_layer(self.drug_embedding, dim_pass))*2, \
self.disease_embedding], axis=1), W0) + b0), dim=1)
sideeffect_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.sideeffect_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.sideeffect_embedding], axis=1), W0) + b0), dim=1)
self.drug_representation = drug_vector1 + drug_vector2
self.protein_representation = protein_vector1 + protein_vector2
self.disease_representation = disease_vector1 + disease_vector2
self.sideeffect_representation = sideeffect_vector1
# reconstructing networks
self.drug_drug_reconstruct = bi_layer(self.drug_representation,
self.drug_representation,
sym=True,
dim_pred=dim_pred)
self.drug_drug_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_drug_reconstruct - self.drug_drug),
(self.drug_drug_reconstruct - self.drug_drug)))
# 药物相似度
self.drug_chemical_reconstruct = bi_layer(self.drug_representation,
self.drug_representation,
sym=True,
dim_pred=dim_pred)
self.drug_chemical_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_chemical_reconstruct - self.drug_chemical),
(self.drug_chemical_reconstruct - self.drug_chemical)))
self.drug_disease_reconstruct = bi_layer(self.drug_representation,
self.disease_representation,
sym=False,
dim_pred=dim_pred)
self.drug_disease_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_disease_reconstruct - self.drug_disease),
(self.drug_disease_reconstruct - self.drug_disease)))
self.drug_sideeffect_reconstruct = bi_layer(
self.drug_representation,
self.sideeffect_representation,
sym=False,
dim_pred=dim_pred)
self.drug_sideeffect_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.drug_sideeffect_reconstruct - self.drug_sideeffect),
(self.drug_sideeffect_reconstruct - self.drug_sideeffect)))
self.protein_protein_reconstruct = bi_layer(
self.protein_representation,
self.protein_representation,
sym=True,
dim_pred=dim_pred)
self.protein_protein_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_protein_reconstruct - self.protein_protein),
(self.protein_protein_reconstruct - self.protein_protein)))
self.protein_sequence_reconstruct = bi_layer(
self.protein_representation,
self.protein_representation,
sym=True,
dim_pred=dim_pred)
self.protein_sequence_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_sequence_reconstruct - self.protein_sequence),
(self.protein_sequence_reconstruct - self.protein_sequence)))
self.protein_disease_reconstruct = bi_layer(
self.protein_representation,
self.disease_representation,
sym=False,
dim_pred=dim_pred)
self.protein_disease_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_disease_reconstruct - self.protein_disease),
(self.protein_disease_reconstruct - self.protein_disease)))
# 药物蛋白质重构
self.drug_protein_reconstruct = bi_layer(self.drug_representation,
self.protein_representation,
sym=False,
dim_pred=dim_pred)
tmp = tf.multiply(self.drug_protein_mask,
(self.drug_protein_reconstruct - self.drug_protein))
self.drug_protein_reconstruct_loss = tf.reduce_sum(
tf.multiply(tmp, tmp))
self.l2_loss = tf.add_n(tf.get_collection("l2_reg"))
self.loss = self.drug_protein_reconstruct_loss + 1.0 * (
self.drug_drug_reconstruct_loss +
self.drug_chemical_reconstruct_loss +
self.drug_disease_reconstruct_loss +
self.drug_sideeffect_reconstruct_loss +
self.protein_protein_reconstruct_loss +
self.protein_sequence_reconstruct_loss +
self.protein_disease_reconstruct_loss) + self.l2_loss
return self.drug_protein_reconstruct, self.loss, self.drug_protein_reconstruct_loss
def inception_v2(width, height, learning_rate):
num_classes = 17
dropout_keep_prob = 0.8
network = input_data(shape=[None, width, height, 3])
conv1a_3_3 = relu(
batch_normalization(
conv_2d(network,
32,
3,
strides=2,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(
batch_normalization(
conv_2d(conv1a_3_3,
32,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(
batch_normalization(
conv_2d(conv2a_3_3,
64,
3,
bias=False,
activation=None,
name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_3a_3x3')
conv3b_1_1 = relu(
batch_normalization(
conv_2d(maxpool3a_3_3,
80,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(
batch_normalization(
conv_2d(conv3b_1_1,
192,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_5a_3x3')
tower_conv = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
96,
1,
bias=False,
activation=None,
name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
48,
1,
bias=False,
activation=None,
name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
64,
5,
bias=False,
activation=None,
name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3,
3,
strides=1,
padding='same',
name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(
batch_normalization(
conv_2d(tower_pool3_0,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge(
[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1],
mode='concat',
axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
tower_conv = relu(
batch_normalization(
conv_2d(net,
384,
3,
bias=False,
strides=2,
activation=None,
padding='VALID',
name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
256,
3,
bias=False,
activation=None,
name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(
batch_normalization(
conv_2d(tower_conv,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1,
288,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='COnv2d_1a_3x3')))
tower_conv2 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2,
288,
3,
bias=False,
name='Conv2d_0b_3x3',
activation=None)))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
320,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool],
mode='concat',
axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(
batch_normalization(
conv_2d(net,
1536,
1,
bias=False,
activation=None,
name='Conv2d_7b_1x1')))
net = avg_pool_2d(net,
net.get_shape().as_list()[1:3],
strides=2,
padding='VALID',
name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes, activation='softmax')
network = tflearn.regression(loss,
optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=learning_rate)
#learning_rate=0.0001)
model = tflearn.DNN(network,
checkpoint_path='inception_resnet_v2',
max_checkpoints=1,
tensorboard_verbose=2,
tensorboard_dir="./tflearn_logs/")
return model
def _model5():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
num_classes = len(yTest[0])
dropout_keep_prob = 0.8
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3')
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3')
####
net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1')))
net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes,activation='softmax')
network = tflearn.regression(loss, optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2',
max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/")
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(299, 299))
num_classes = 17
dropout_keep_prob = 0.8
network = input_data(shape=[None, 299, 299, 3])
conv1a_3_3 = relu(batch_normalization(
conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID', activation=None, name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(
batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID', activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(
conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID', activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(
conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID', activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(
batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(
batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
def _build_model(self):
# inputs
self.drug_drug = tf.constant(drug_drug, tf.float32)
self.drug_drug_normalize = tf.constant(drug_drug_normalize, tf.float32,
[num_drug, num_drug])
self.drug_chemical = tf.constant(drug_chemical, tf.float32,
[num_drug, num_drug])
self.drug_chemical_normalize = tf.constant(drug_chemical_normalize,
tf.float32,
[num_drug, num_drug])
self.drug_disease = tf.constant(drug_disease, tf.float32,
[num_drug, num_disease])
self.drug_disease_normalize = tf.constant(drug_disease_normalize,
tf.float32,
[num_drug, num_disease])
self.drug_sideeffect = tf.constant(drug_sideeffect, tf.float32,
[num_drug, num_sideeffect])
self.drug_sideeffect_normalize = tf.constant(
drug_sideeffect_normalize, tf.float32, [num_drug, num_sideeffect])
self.protein_protein = tf.constant(protein_protein, tf.float32,
[num_protein, num_protein])
self.protein_protein_normalize = tf.constant(
protein_protein_normalize, tf.float32, [num_protein, num_protein])
self.protein_sequence = tf.constant(protein_sequence, tf.float32,
[num_protein, num_protein])
self.protein_sequence_normalize = tf.constant(
protein_sequence_normalize, tf.float32, [num_protein, num_protein])
self.protein_disease = tf.constant(protein_disease, tf.float32,
[num_protein, num_disease])
self.protein_disease_normalize = tf.constant(
protein_disease_normalize, tf.float32, [num_protein, num_disease])
self.disease_drug = tf.constant(disease_drug, tf.float32,
[num_disease, num_drug])
self.disease_drug_normalize = tf.constant(disease_drug_normalize,
tf.float32,
[num_disease, num_drug])
self.disease_protein = tf.constant(disease_protein, tf.float32,
[num_disease, num_protein])
self.disease_protein_normalize = tf.constant(
disease_protein_normalize, tf.float32, [num_disease, num_protein])
self.sideeffect_drug = tf.constant(sideeffect_drug, tf.float32,
[num_sideeffect, num_drug])
self.sideeffect_drug_normalize = tf.constant(
sideeffect_drug_normalize, tf.float32, [num_sideeffect, num_drug])
self.drug_protein = tf.placeholder(tf.float32, [num_drug, num_protein],
name='drug_protein')
self.drug_protein_normalize = tf.placeholder(
tf.float32, [num_drug, num_protein], name='drug_protein_normalize')
self.protein_drug = tf.placeholder(tf.float32, [num_protein, num_drug],
name='protein_drug')
self.protein_drug_normalize = tf.placeholder(
tf.float32, [num_protein, num_drug], name='protein_drug_normalize')
self.drug_protein_mask = tf.placeholder(tf.float32,
[num_drug, num_protein],
name='drug_protein_mask')
# features
# 随机生成embedding
self.drug_embedding = weight_variable([num_drug, dim_drug])
self.protein_embedding = weight_variable([num_protein, dim_protein])
self.disease_embedding = weight_variable([num_disease, dim_disease])
self.sideeffect_embedding = weight_variable(
[num_sideeffect, dim_sideeffect])
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.drug_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.protein_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.disease_embedding))
tf.add_to_collection(
'l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(self.sideeffect_embedding))
# feature passing weights (maybe different types of nodes can use different weights)
W0 = weight_variable([dim_pass + dim_drug, dim_drug], name='W0')
b0 = bias_variable([dim_drug], name='b0')
tf.add_to_collection('l2_reg',
tf.contrib.layers.l2_regularizer(1.0)(W0))
# passing 1 times (can be easily extended to multiple passes)
drug_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.drug_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_chemical_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.drug_sideeffect_normalize, a_layer(self.sideeffect_embedding, dim_pass)) + \
tf.matmul(self.drug_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.drug_embedding], axis=1), W0) + b0), dim=1, name="drug_vector1")
drug_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.drug_drug_normalize, a_layer(self.drug_embedding, dim_pass)) * 4, \
self.drug_embedding], axis=1), W0) + b0), dim=1, name="drug_vector2")
protein_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.protein_protein_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_sequence_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.protein_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.protein_embedding], axis=1), W0) + b0), dim=1, name="protein_vector1")
# 改
protein_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.protein_protein_normalize, a_layer(self.protein_embedding, dim_pass)) * 4, \
self.protein_embedding], axis=1), W0) + b0), dim=1, name="protein_vector2")
disease_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.disease_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.disease_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.disease_embedding], axis=1), W0) + b0), dim=1, name="disease_vector1")
disease_vector2 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.disease_drug_normalize, a_layer(self.drug_embedding, dim_pass)) * 2, \
self.disease_embedding], axis=1), W0) + b0), dim=1, name="disease_vector2")
sideeffect_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.sideeffect_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.sideeffect_embedding], axis=1), W0) + b0), dim=1, name="sideeffect_vector1")
self.drug_representation = drug_vector1 + drug_vector2
self.protein_representation = protein_vector1 + protein_vector2
self.disease_representation = disease_vector1 + disease_vector2
self.sideeffect_representation = sideeffect_vector1
# print(type(self.drug_representation))
# reconstructing networks
self.drug_drug_reconstruct = bi_layer(self.drug_representation,
self.drug_representation,
sym=True,
dim_pred=dim_pred,
name='drug_drug_reconstruct')
self.drug_drug_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_drug_reconstruct - self.drug_drug),
(self.drug_drug_reconstruct - self.drug_drug)))
# 药物相似度
self.drug_chemical_reconstruct = bi_layer(
self.drug_representation,
self.drug_representation,
sym=True,
dim_pred=dim_pred,
name='drug_chemical_reconstruct')
self.drug_chemical_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_chemical_reconstruct - self.drug_chemical),
(self.drug_chemical_reconstruct - self.drug_chemical)))
self.drug_disease_reconstruct = bi_layer(
self.drug_representation,
self.disease_representation,
sym=False,
dim_pred=dim_pred,
name='drug_disease_reconstruct')
self.drug_disease_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.drug_disease_reconstruct - self.drug_disease),
(self.drug_disease_reconstruct - self.drug_disease)))
self.drug_sideeffect_reconstruct = bi_layer(
self.drug_representation,
self.sideeffect_representation,
sym=False,
dim_pred=dim_pred,
name='drug_sideeffect_reconstruct')
self.drug_sideeffect_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.drug_sideeffect_reconstruct - self.drug_sideeffect),
(self.drug_sideeffect_reconstruct - self.drug_sideeffect)))
self.protein_protein_reconstruct = bi_layer(
self.protein_representation,
self.protein_representation,
sym=True,
dim_pred=dim_pred,
name='protein_protein_reconstruct')
self.protein_protein_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_protein_reconstruct - self.protein_protein),
(self.protein_protein_reconstruct - self.protein_protein)))
self.protein_sequence_reconstruct = bi_layer(
self.protein_representation,
self.protein_representation,
sym=True,
dim_pred=dim_pred,
name='protein_sequence_reconstruct')
self.protein_sequence_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_sequence_reconstruct - self.protein_sequence),
(self.protein_sequence_reconstruct - self.protein_sequence)))
self.protein_disease_reconstruct = bi_layer(
self.protein_representation,
self.disease_representation,
sym=False,
dim_pred=dim_pred,
name='protein_disease_reconstruct')
self.protein_disease_reconstruct_loss = tf.reduce_sum(
tf.multiply(
(self.protein_disease_reconstruct - self.protein_disease),
(self.protein_disease_reconstruct - self.protein_disease)))
self.drug_protein_reconstruct = bi_layer(
self.drug_representation,
self.protein_representation,
sym=False,
dim_pred=dim_pred,
name='drug_protein_reconstruct')
tmp = tf.multiply(self.drug_protein_mask,
(self.drug_protein_reconstruct - self.drug_protein))
self.drug_protein_reconstruct_loss = tf.reduce_sum(
tf.multiply(tmp, tmp))
self.l2_loss = tf.add_n(tf.get_collection("l2_reg"))
self.loss = self.drug_protein_reconstruct_loss + 1.0 * (
self.drug_drug_reconstruct_loss +
self.drug_chemical_reconstruct_loss +
self.drug_disease_reconstruct_loss +
self.drug_sideeffect_reconstruct_loss +
self.protein_protein_reconstruct_loss +
self.protein_sequence_reconstruct_loss +
self.protein_disease_reconstruct_loss) + self.l2_loss
def a_layer(x, units):
W = weight_variable([x.get_shape().as_list()[1], units])
b = bias_variable([units])
tf.add_to_collection('l2_reg', tf.contrib.layers.l2_regularizer(1.0)(W))
return relu(tf.matmul(x, W) + b)
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(299, 299))
num_classes = 17
dropout_keep_prob = 0.8
network = input_data(shape=[None, 299, 299, 3])
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
def network(img_shape, name, LR):
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
#
# # Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_blur (sigma_max=3.0)
img_aug.add_random_flip_leftright()
img_aug.add_random_flip_updown()
img_aug.add_random_90degrees_rotation(rotations=[0, 2])
network = input_data(shape=img_shape, name=name, data_preprocessing=img_prep, data_augmentation=img_aug )
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
# tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
# tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None)))
# tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
# tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3')
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3')
net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1')))
net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes,activation='softmax')
network = tflearn.regression(loss, optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001, name='targets')
return network
def _build_model(self):
# inputs
self.RD = tf.placeholder(tf.float32, [num_R, num_D])
self.RD_normalize = tf.placeholder(tf.float32, [num_R, num_D])
self.DR = tf.placeholder(tf.float32, [num_D, num_R])
self.DR_normalize = tf.placeholder(tf.float32, [num_D, num_R])
self.DS1 = tf.placeholder(tf.float32, [num_D, num_D])
self.DS2 = tf.placeholder(tf.float32, [num_D, num_D])
self.DSfc = tf.placeholder(tf.float32, [num_D, num_D])
self.DSgs = tf.placeholder(tf.float32, [num_D, num_D])
self.DS_normalize1 = tf.placeholder(tf.float32, [num_D, num_D])
self.DS_normalize2 = tf.placeholder(tf.float32, [num_D, num_D])
self.DSfc_normalize = tf.placeholder(tf.float32, [num_D, num_D])
self.DSgs_normalize = tf.placeholder(tf.float32, [num_D, num_D])
self.RS = tf.placeholder(tf.float32, [num_R, num_R])
self.RSgs = tf.placeholder(tf.float32, [num_R, num_R])
self.RSsq = tf.placeholder(tf.float32, [num_R, num_R])
self.RS_normalize = tf.placeholder(tf.float32, [num_R, num_R])
self.RSgs_normalize = tf.placeholder(tf.float32, [num_R, num_R])
self.RSsq_normalize = tf.placeholder(tf.float32, [num_R, num_R])
self.RSmask = tf.placeholder(tf.float32, [num_R, num_R])
self.DSmask1 = tf.placeholder(tf.float32, [num_D, num_D])
self.DSmask2 = tf.placeholder(tf.float32, [num_D, num_D])
self.RD_mask = tf.placeholder(tf.float32, [num_R, num_D])
# features
self.Dembedding = weight_variable([num_D, dim_D])
self.Rembedding = weight_variable([num_R, dim_R])
W0 = weight_variable([dim_pass + dim_R, dim_R])
b0 = bias_variable([dim_R])
# passing 1 times (can be easily extended to multiple passes)
R_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.RD_normalize, a_layer(self.Dembedding, dim_pass)) + \
tf.matmul(self.RS_normalize, a_layer(self.Rembedding, dim_pass))+ \
tf.matmul(self.RSgs_normalize, a_layer(self.Rembedding, dim_pass))+ \
tf.matmul(self.RSsq_normalize, a_layer(self.Rembedding, dim_pass)), \
self.Rembedding], axis=1), W0) + b0), dim=1)
D_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.DR_normalize, a_layer(self.Rembedding, dim_pass)) + \
tf.matmul(self.DS_normalize1, a_layer(self.Dembedding, dim_pass))+ \
tf.matmul(self.DS_normalize2, a_layer(self.Dembedding, dim_pass))+ \
tf.matmul(self.DSgs_normalize, a_layer(self.Dembedding, dim_pass))+ \
tf.matmul(self.DSfc_normalize, a_layer(self.Dembedding, dim_pass)), \
self.Dembedding], axis=1), W0) + b0), dim=1)
self.R_representation = R_vector1
self.D_representation = D_vector1
# reconstructing networks
self.R_reconstruct = bi_layer(self.R_representation, self.R_representation, sym=True, dim_pred=dim_pred)
RStmp = (self.R_reconstruct - self.RS)
#RStmp = tf.multiply(self.RSmask, (self.R_reconstruct - self.RS))
# RStempu = tf.multiply((tf.ones((num_R, num_R)) - self.RSmask), (self.R_reconstruct - self.RS))
self.R_reconstruct_loss = tf.reduce_sum(tf.multiply(RStmp, RStmp))# +0.21*tf.reduce_sum(tf.multiply(RStempu, RStempu))
self.Rgs_reconstruct = bi_layer(self.R_representation, self.R_representation, sym=True, dim_pred=dim_pred)
RSgstmp = (self.Rgs_reconstruct - self.RSgs)
#RSgstmp = tf.multiply(self.RSmask, (self.R_reconstruct - self.RSgs))
# RStempu = tf.multiply((tf.ones((num_R, num_R)) - self.RSmask), (self.R_reconstruct - self.RS))
self.Rgs_reconstruct_loss = tf.reduce_sum(tf.multiply(RSgstmp, RSgstmp))
self.Rsq_reconstruct = bi_layer(self.R_representation, self.R_representation, sym=True, dim_pred=dim_pred)
RSsqtmp = (self.Rsq_reconstruct - self.RSsq)
#RSfctmp = tf.multiply(self.RSmask, (self.R_reconstruct - self.RSgs))
# RStempu = tf.multiply((tf.ones((num_R, num_R)) - self.RSmask), (self.R_reconstruct - self.RS))
self.Rsq_reconstruct_loss = tf.reduce_sum(tf.multiply(RSsqtmp, RSsqtmp))
self.D_reconstruct1 = bi_layer(self.D_representation, self.D_representation, sym=True, dim_pred=dim_pred)
#DStmp1 = tf.multiply(self.DSmask1, (self.D_reconstruct1 - self.DS1))
DStmp1 = (self.D_reconstruct1 - self.DS1)
DStempu1 = tf.multiply((tf.ones((num_D, num_D)) - self.DS1), (self.D_reconstruct1 - self.DS1))
self.D_reconstruct_loss1 = tf.reduce_sum(tf.multiply(DStmp1, DStmp1)) #+0.1*tf.reduce_sum(tf.multiply(DStempu1, DStempu1))
self.D_reconstruct2 = bi_layer(self.D_representation, self.D_representation, sym=True, dim_pred=dim_pred)
#DStmp2 = tf.multiply(self.DSmask2, (self.D_reconstruct2 - self.DS2))
DStmp2 = (self.D_reconstruct2 - self.DS2)
DStempu2 = tf.multiply((tf.ones((num_D, num_D)) - self.DSmask2), (self.D_reconstruct2 - self.DS2))
self.D_reconstruct_loss2 = tf.reduce_sum(tf.multiply(DStmp2, DStmp2)) #+0.1*tf.reduce_sum(tf.multiply(DStempu2, DStempu2))
self.Dfc_reconstruct = bi_layer(self.D_representation, self.D_representation, sym=True, dim_pred=dim_pred)
#DStmp1 = tf.multiply(self.DSmask1, (self.D_reconstruct1 - self.DS_normalize1))
DSfctmp = (self.Dfc_reconstruct - self.DSfc)
#DStempu1 = tf.multiply((tf.ones((num_D, num_D)) - self.DS1), (self.D_reconstruct1 - self.DS1))
self.Dfc_reconstruct_loss = tf.reduce_sum(tf.multiply(DSfctmp, DSfctmp))
self.Dgs_reconstruct = bi_layer(self.D_representation, self.D_representation, sym=True, dim_pred=dim_pred)
#DSgstmp = tf.multiply(self.DSmask1, (self.D_reconstruct1 - self.DS_normalize1))
DSgstmp = (self.Dgs_reconstruct - self.DSgs)
#DStempu1 = tf.multiply((tf.ones((num_D, num_D)) - self.DS1), (self.D_reconstruct1 - self.DS1))
self.Dgs_reconstruct_loss = tf.reduce_sum(tf.multiply(DSgstmp, DSgstmp))
self.RD_reconstruct = bi_layer(self.R_representation, self.D_representation, sym=False, dim_pred=dim_pred)
tmp = tf.multiply(self.RD_mask, (self.RD_reconstruct - self.RD))
tmp = self.RD_reconstruct - self.RD
tmpu = tf.multiply((tf.ones((num_R, num_D)) - self.RD_mask), (self.RD_reconstruct - self.RD))
self.DR_reconstruct_loss = tf.reduce_sum(tf.multiply(tmp, tmp))#+0.03*tf.reduce_sum(tf.multiply(tmpu, tmpu))
self.loss = self.DR_reconstruct_loss + 2* (
(self.R_reconstruct_loss+self.Rsq_reconstruct_loss+self.Rgs_reconstruct_loss) +
(self.D_reconstruct_loss1+ self.D_reconstruct_loss2+ self.Dfc_reconstruct_loss+ self.Dgs_reconstruct_loss)) # + tf.reduce_sum(tf.multiply(W0,W0 ))+ tf.reduce_sum(tf.multiply(b0,b0 ))
def _build_model(self):
#inputs
self.rna_rna = tf.placeholder(tf.float32, [num_rna, num_rna])
self.rna_rna_normalize = tf.placeholder(tf.float32, [num_rna, num_rna])
self.dis_dis = tf.placeholder(tf.float32, [num_dis, num_dis])
self.dis_dis_normalize = tf.placeholder(tf.float32, [num_dis, num_dis])
self.rna_dis = tf.placeholder(tf.float32, [num_rna, num_dis])
self.rna_dis_normalize = tf.placeholder(tf.float32, [num_rna, num_dis])
self.dis_rna = tf.placeholder(tf.float32, [num_dis, num_rna])
self.dis_rna_normalize = tf.placeholder(tf.float32, [num_dis, num_rna])
self.rna_dis_mask = tf.placeholder(tf.float32, [num_rna, num_dis])
#features
self.rna_embedding = weight_variable([num_rna, dim_rna])
self.dis_embedding = weight_variable([num_dis, dim_dis])
#feature passing weights (maybe different types of nodes can use different weights)
W0 = weight_variable([dim_pass + dim_rna, dim_rna])
b0 = bias_variable([dim_rna])
rna_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([\
tf.matmul(self.rna_rna_normalize, a_layer(self.rna_embedding, dim_pass)) + \
tf.matmul(self.rna_dis_normalize, a_layer(self.dis_embedding, dim_pass)), \
self.rna_embedding], axis=1), W0)+b0),dim=1)
dis_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([\
tf.matmul(self.dis_dis_normalize, a_layer(self.dis_embedding, dim_pass)) + \
tf.matmul(self.dis_rna_normalize, a_layer(self.rna_embedding, dim_pass)) , \
self.dis_embedding], axis=1), W0)+b0),dim=1)
self.rna_representation = rna_vector1
self.dis_representation = dis_vector1
#reconstructing networks
self.rna_rna_reconstruct = bi_layer(self.rna_representation,
self.rna_representation,
sym=True,
dim_pred=dim_pred)
self.rna_rna_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.rna_rna_reconstruct - self.rna_rna),
(self.rna_rna_reconstruct - self.rna_rna)))
self.dis_dis_reconstruct = bi_layer(self.dis_representation,
self.dis_representation,
sym=True,
dim_pred=dim_pred)
self.dis_dis_reconstruct_loss = tf.reduce_sum(
tf.multiply((self.dis_dis_reconstruct - self.dis_dis),
(self.dis_dis_reconstruct - self.dis_dis)))
self.rna_dis_reconstruct = bi_layer(self.rna_representation,
self.dis_representation,
sym=False,
dim_pred=dim_pred)
tmp = tf.multiply(self.rna_dis_mask,
(self.rna_dis_reconstruct - self.rna_dis))
self.rna_dis_reconstruct_loss = tf.reduce_sum(tf.multiply(tmp, tmp))
self.loss = self.rna_dis_reconstruct_loss + 1.0*(self.rna_rna_reconstruct_loss + \
self.dis_dis_reconstruct_loss)
def _build_model(self):
#inputs
self.drug_drug = tf.placeholder(tf.float32, [num_drug, num_drug])
self.drug_drug_normalize = tf.placeholder(tf.float32, [num_drug, num_drug])
self.drug_chemical = tf.placeholder(tf.float32, [num_drug, num_drug])
self.drug_chemical_normalize = tf.placeholder(tf.float32, [num_drug, num_drug])
self.drug_disease = tf.placeholder(tf.float32, [num_drug, num_disease])
self.drug_disease_normalize = tf.placeholder(tf.float32, [num_drug, num_disease])
self.drug_sideeffect = tf.placeholder(tf.float32, [num_drug, num_sideeffect])
self.drug_sideeffect_normalize = tf.placeholder(tf.float32, [num_drug, num_sideeffect])
self.protein_protein = tf.placeholder(tf.float32, [num_protein, num_protein])
self.protein_protein_normalize = tf.placeholder(tf.float32, [num_protein, num_protein])
self.protein_sequence = tf.placeholder(tf.float32, [num_protein, num_protein])
self.protein_sequence_normalize = tf.placeholder(tf.float32, [num_protein, num_protein])
self.protein_disease = tf.placeholder(tf.float32, [num_protein, num_disease])
self.protein_disease_normalize = tf.placeholder(tf.float32, [num_protein, num_disease])
self.disease_drug = tf.placeholder(tf.float32, [num_disease, num_drug])
self.disease_drug_normalize = tf.placeholder(tf.float32, [num_disease, num_drug])
self.disease_protein = tf.placeholder(tf.float32, [num_disease, num_protein])
self.disease_protein_normalize = tf.placeholder(tf.float32, [num_disease, num_protein])
self.sideeffect_drug = tf.placeholder(tf.float32, [num_sideeffect, num_drug])
self.sideeffect_drug_normalize = tf.placeholder(tf.float32, [num_sideeffect, num_drug])
self.drug_protein = tf.placeholder(tf.float32, [num_drug, num_protein])
self.drug_protein_normalize = tf.placeholder(tf.float32, [num_drug, num_protein])
self.protein_drug = tf.placeholder(tf.float32, [num_protein, num_drug])
self.protein_drug_normalize = tf.placeholder(tf.float32, [num_protein, num_drug])
self.drug_protein_mask = tf.placeholder(tf.float32, [num_drug, num_protein])
#features
self.drug_embedding = weight_variable([num_drug,dim_drug])
self.protein_embedding = weight_variable([num_protein,dim_protein])
self.disease_embedding = weight_variable([num_disease,dim_disease])
self.sideeffect_embedding = weight_variable([num_sideeffect,dim_sideeffect])
#feature passing weights (maybe different types of nodes can use different weights)
W0 = weight_variable([dim_pass+dim_drug, dim_drug])
b0 = bias_variable([dim_drug])
#passing 1 times (can be easily extended to multiple passes)
drug_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.drug_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_chemical_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.drug_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.drug_sideeffect_normalize, a_layer(self.sideeffect_embedding, dim_pass)) + \
tf.matmul(self.drug_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.drug_embedding], axis=1), W0)+b0),dim=1)
protein_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.protein_protein_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_sequence_normalize, a_layer(self.protein_embedding, dim_pass)) + \
tf.matmul(self.protein_disease_normalize, a_layer(self.disease_embedding, dim_pass)) + \
tf.matmul(self.protein_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.protein_embedding], axis=1), W0)+b0),dim=1)
disease_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.disease_drug_normalize, a_layer(self.drug_embedding, dim_pass)) + \
tf.matmul(self.disease_protein_normalize, a_layer(self.protein_embedding, dim_pass)), \
self.disease_embedding], axis=1), W0)+b0),dim=1)
sideeffect_vector1 = tf.nn.l2_normalize(relu(tf.matmul(
tf.concat([tf.matmul(self.sideeffect_drug_normalize, a_layer(self.drug_embedding, dim_pass)), \
self.sideeffect_embedding], axis=1), W0)+b0),dim=1)
self.drug_representation = drug_vector1
self.protein_representation = protein_vector1
self.disease_representation = disease_vector1
self.sideeffect_representation = sideeffect_vector1
#reconstructing networks
self.drug_drug_reconstruct = bi_layer(self.drug_representation,self.drug_representation, sym=True, dim_pred=dim_pred)
self.drug_drug_reconstruct_loss = tf.reduce_sum(tf.multiply((self.drug_drug_reconstruct-self.drug_drug), (self.drug_drug_reconstruct-self.drug_drug)))
self.drug_chemical_reconstruct = bi_layer(self.drug_representation,self.drug_representation, sym=True, dim_pred=dim_pred)
self.drug_chemical_reconstruct_loss = tf.reduce_sum(tf.multiply((self.drug_chemical_reconstruct-self.drug_chemical), (self.drug_chemical_reconstruct-self.drug_chemical)))
self.drug_disease_reconstruct = bi_layer(self.drug_representation,self.disease_representation, sym=False, dim_pred=dim_pred)
self.drug_disease_reconstruct_loss = tf.reduce_sum(tf.multiply((self.drug_disease_reconstruct-self.drug_disease), (self.drug_disease_reconstruct-self.drug_disease)))
self.drug_sideeffect_reconstruct = bi_layer(self.drug_representation,self.sideeffect_representation, sym=False, dim_pred=dim_pred)
self.drug_sideeffect_reconstruct_loss = tf.reduce_sum(tf.multiply((self.drug_sideeffect_reconstruct-self.drug_sideeffect), (self.drug_sideeffect_reconstruct-self.drug_sideeffect)))
self.protein_protein_reconstruct = bi_layer(self.protein_representation,self.protein_representation, sym=True, dim_pred=dim_pred)
self.protein_protein_reconstruct_loss = tf.reduce_sum(tf.multiply((self.protein_protein_reconstruct-self.protein_protein), (self.protein_protein_reconstruct-self.protein_protein)))
self.protein_sequence_reconstruct = bi_layer(self.protein_representation,self.protein_representation, sym=True, dim_pred=dim_pred)
self.protein_sequence_reconstruct_loss = tf.reduce_sum(tf.multiply((self.protein_sequence_reconstruct-self.protein_sequence), (self.protein_sequence_reconstruct-self.protein_sequence)))
self.protein_disease_reconstruct = bi_layer(self.protein_representation,self.disease_representation, sym=False, dim_pred=dim_pred)
self.protein_disease_reconstruct_loss = tf.reduce_sum(tf.multiply((self.protein_disease_reconstruct-self.protein_disease), (self.protein_disease_reconstruct-self.protein_disease)))
self.drug_protein_reconstruct = bi_layer(self.drug_representation,self.protein_representation, sym=False, dim_pred=dim_pred)
tmp = tf.multiply(self.drug_protein_mask, (self.drug_protein_reconstruct-self.drug_protein))
self.drug_protein_reconstruct_loss = tf.reduce_sum(tf.multiply(tmp, tmp))
self.loss = self.drug_protein_reconstruct_loss + 1.0*(self.drug_drug_reconstruct_loss+self.drug_chemical_reconstruct_loss+
self.drug_disease_reconstruct_loss+self.drug_sideeffect_reconstruct_loss+
self.protein_protein_reconstruct_loss+self.protein_sequence_reconstruct_loss+
self.protein_disease_reconstruct_loss)
def _model5():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
num_classes = len(Y[0])
dropout_keep_prob = 0.8
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3')
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3')
####
net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1')))
net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes,activation='softmax')
network = tflearn.regression(loss, optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2',
max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/")
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True,
show_metric=True, batch_size=batchNum, snapshot_step=2000,
snapshot_epoch=False, run_id='inception_resnet_v2_oxflowers17')
if modelStore: model.save(_id + '-model.tflearn')
