def __init__(self, learning_rate, input_shape, BS):#input_shape example: [BS,1,28,28]
self.lr = learning_rate
self.conv2d_1 = ly.conv2d(input_shape, [5, 5, 1, 6], [2, 2], 'VALID')
self.relu_1 = ly.relu()
# conv2 : 6*12*12 - > 10*5*5
self.conv2d_2 = ly.conv2d([BS, 6, 12, 12], [3, 3, 6, 10], [2, 2], 'VALID')
self.relu_2 = ly.relu()
self.flatter = ly.flatter()
self.full_connect_1 = ly.full_connect(250, 84)
self.relu_3 = ly.relu()
self.full_connect_2 = ly.full_connect(84, 10)
self.loss_func = ly.softmax_cross_entropy_error()
python类conv2d()的实例源码
conv2_fc2_dropout_cross_entropy_net.py 文件源码
项目：numpy_cnn
作者: Ryanshuai
项目源码
文件源码
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def __init__(self, learning_rate, input_shape, BS):#input_shape example: [BS,1,28,28]
self.lr = learning_rate
self.conv2d_1 = ly.conv2d(input_shape, [5, 5, 1, 6], [1, 1], 'VALID')
self.relu_1 = ly.relu()
self.conv2d_2 = ly.conv2d([BS, 6, 12, 12], [3, 3, 6, 10], [2, 2], 'VALID')
self.relu_2 = ly.relu()
self.flatter = ly.flatter()
self.full_connect_1 = ly.full_connect(250, 84)
self.relu_3 = ly.relu()
self.dropout = ly.dropout(lenth=84)
self.full_connect_2 = ly.full_connect(84, 10)
self.loss_func = ly.softmax_cross_entropy_error()
def __init__(self, learning_rate, input_shape, BS):#input_shape example: [BS,1,28,28]
self.lr = learning_rate
self.conv2d_1 = ly.conv2d(input_shape,[5,5,1,32],[1,1])
self.relu_1 = ly.relu()
self.max_pool_1 = ly.max_pooling(self.conv2d_1.output_shape, filter_shape=[2,2], strides=[2,2])
self.conv2d_2 = ly.conv2d(self.max_pool_1.output_shape,[5,5,32,64],[1,1])
self.relu_2 = ly.relu()
self.max_pool_2 = ly.max_pooling(self.conv2d_2.output_shape, filter_shape=[2,2], strides=[2,2])
self.flatter = ly.flatter()
self.full_connect_1 = ly.full_connect(input_len=7*7*64,output_len=1024)
self.relu_3 = ly.relu()
self.dropout_1 = ly.dropout(1024)
self.full_connect_2 = ly.full_connect(input_len=1024,output_len=10)
self.loss_func = ly.softmax_cross_entropy_error()
c2_p2_f2_dropout_cross_entropy_net.py 文件源码
项目：numpy_cnn
作者: Ryanshuai
项目源码
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def __init__(self, learning_rate, input_shape):#input_shape example: [BS,1,28,28]
self.lr = learning_rate
# conv1:(BS,1,28,28)->(BS,6,28,28)->(BS,6,14,14)
self.conv2d_1 = ly.conv2d(input_shape, [5, 5, 1, 6], [1, 1], 'SAME')
self.relu_1 = ly.relu()
self.pool_1 = ly.max_pooling(self.conv2d_1.output_shape, [2,2], [2,2], 'SAME')
# conv2:(BS,6,14,14)->(BS,10,14,14)->(BS,10,7,7)
self.conv2d_2 = ly.conv2d(self.pool_1.output_shape, [5, 5, 6, 10], [1, 1], 'SAME')
self.relu_2 = ly.relu()
self.pool_2 = ly.max_pooling(self.conv2d_2.output_shape, [2,2], [2,2], 'SAME')
# flat:(BS,10,7,7)->(BS,490)
self.flatter = ly.flatter()
# fc1:(BS,490)->(BS,84)
self.full_connect_1 = ly.full_connect(490, 84)
self.relu_3 = ly.relu()
self.dropout = ly.dropout(lenth=84)
# fc2:(BS,84)->(BS,10)
self.full_connect_2 = ly.full_connect(84, 10)
self.loss_func = ly.softmax_cross_entropy_error()
def __call__(self, inputs):
with tf.name_scope(self.name):
with tf.name_scope('preprocessing'):
pad_1 = tf.pad(inputs, np.array([[0,0],[2,2],[2,2],[0,0]]))
conv_1 = conv2d(pad_1, 64, kernel_size=6, strides = 2, name = '256to128')
res_1 = residual(conv_1, 128)
pool_1 = tf.contrib.layers.max_pool2d(res_1, [2,2], [2,2], padding= 'VALID')
res_2 = residual(pool_1, 128)
res_3 = residual(res_2, self.nFeat)
# Supervision Table
hg = [None] * self.nbStack
ll = [None] * self.nbStack
ll_ = [None] * self.nbStack
drop = [None] * self.nbStack
out = [None] * self.nbStack
out_ = [None] * self.nbStack
sum_ = [None] * self.nbStack
with tf.name_scope('stacks'):
with tf.name_scope('hourglass.1'):
hg[0] = self.hourglass(res_3, self.nLow, self.nFeat, 'hourglass')
ll[0] = convBnrelu(hg[0], self.nFeat, name= 'conv_1')
ll_[0] = conv2d(ll[0],self.nFeat,1,1,'VALID','ll')
drop[0] = tf.layers.dropout(ll_[0], rate = 0.1, training = self.train)
out[0] = conv2d(ll[0],self.outDim,1,1,'VALID','out')
out_[0] = conv2d(out[0],self.nFeat,1,1,'VALID','out_')
sum_[0] = tf.add_n([drop[0], out_[0], res_3])
for i in range(1, self.nbStack-1):
with tf.name_scope('hourglass.' + str(i+1)):
hg[i] = self.hourglass(sum_[i-1], self.nLow, self.nFeat, 'hourglass')
ll[i] = convBnrelu(hg[i], self.nFeat, name='conv_1')
ll_[i] = conv2d(ll[i],self.nFeat,1,1,'VALID','ll')
drop[i] = tf.layers.dropout(ll_[i],rate=0.1, training = self.train)
out[i] = conv2d(ll[i],self.outDim,1,1,'VALID','out')
out_[i] = conv2d(out[i],self.nFeat,1,1,'VALID','out_')
sum_[i] = tf.add_n([drop[i], out_[i], sum_[i-1]])
with tf.name_scope('hourglass.' + str(self.nbStack)):
hg[self.nbStack-1] = self.hourglass(sum_[self.nbStack - 2], self.nLow, self.nFeat, 'hourglass')
ll[self.nbStack-1] = convBnrelu(hg[self.nbStack - 1], self.nFeat, name='conv_1')
drop[self.nbStack-1] = tf.layers.dropout(ll[self.nbStack-1], rate=0.1, training = self.train)
out[self.nbStack-1] = conv2d(drop[self.nbStack-1],self.outDim,1,1,'VALID', 'out')
return tf.stack(out, name = 'output')
def fcn_net(self, x, train=True):
conv1 = conv2d(x, [3, 3, 3, 32], 'conv1')
maxp1 = maxpooling(conv1)
conv2 = conv2d(maxp1, [3, 3, 32, 32], 'conv2')
maxp2 = maxpooling(conv2)
conv3 = conv2d(maxp2, [3, 3, 32, 64], 'conv3')
maxp3 = maxpooling(conv3)
conv4 = conv2d(maxp3, [3, 3, 64, 64], 'conv4')
maxp4 = maxpooling(conv4)
conv5 = conv2d(maxp4, [3, 3, 64, 128], 'conv5')
maxp5 = maxpooling(conv5)
conv6 = conv2d(maxp5, [3, 3, 128, 128], 'conv6')
maxp6 = maxpooling(conv6)
conv7 = conv2d(maxp6, [3, 3, 128, 256], 'conv7')
maxp7 = maxpooling(conv7)
conv8 = conv2d(maxp7, [3, 3, 256, 256], 'conv8')
maxp8 = maxpooling(conv8)
conv9 = conv2d(maxp8, [3, 3, 256, 512], 'conv9')
maxp9 = maxpooling(conv9)
drop = tf.nn.dropout(maxp9, self.dropout)
# 1x1 convolution + sigmoid activation
net = conv2d(drop, [1, 1, 512, self.input_size*self.input_size], 'conv10', activation='no')
# squeeze the last two dimension in train
if train:
net = tf.squeeze(net, [1, 2], name="squeezed")
return net
def u_net(self, x, layers=4, base_channel=64, train=True):
ds_layers = {}
ds_layer_shape = {}
# down sample layers
for layer in range(0, layers-1):
f_channels = base_channel * (2**layer)
layer_name = 'ds_{}'.format(layer)
if layer == 0:
x = conv2d(x, [3, 3, 3, f_channels], layer_name + '_1')
else:
x = conv2d(x, [3, 3, f_channels/2, f_channels], layer_name + '_1')
x = conv2d(x, [3, 3, f_channels, f_channels], layer_name + '_2')
ds_layers[layer] = x
ds_layer_shape[layer] = tf.shape(x)
x = maxpooling(x)
# bottom layer
f_channels = base_channel * (2**(layers-1))
x = conv2d(x, [3, 3, f_channels/2, f_channels], 'bottom_1')
x = conv2d(x, [3, 3, f_channels, f_channels], 'bottom_2')
# up sample layers
for layer in range(layers-2, -1, -1):
f_channels = base_channel * (2**layer)
layer_name = 'up_{}'.format(layer)
x = deconv2d(x, [3, 3, f_channels, 2*f_channels], ds_layer_shape[layer], layer_name + '_deconv2d')
# add the previous down sumple layer to the up sample layer
x = concat(ds_layers[layer], x)
x = conv2d(x, [3, 3, 2*f_channels, f_channels], layer_name + '_conv_1')
x = conv2d(x, [3, 3, f_channels, f_channels], layer_name + '_conv_2')
#if train:
# x = tf.nn.dropout(x, self.dropout)
# add 1x1 convolution layer to change channel to one
x = conv2d(x, [1, 1, base_channel, 1], 'conv_1x1', activation='no')
logits = tf.squeeze(x, axis=3)
return logits
def _build_encoder(self):
with tf.variable_scope(self.name):
if self.arch == 'FC':
layer_i = layers.flatten(self.input_ph)
for i, layer_size in enumerate(self.fc_layer_sizes):
layer_i = layers.fc('fc{}'.format(i+1), layer_i, layer_size, activation=self.activation)[-1]
self.ox = layer_i
elif self.arch == 'ATARI-TRPO':
self.w1, self.b1, self.o1 = layers.conv2d('conv1', self.input_ph, 16, 4, self.input_channels, 2, activation=self.activation)
self.w2, self.b2, self.o2 = layers.conv2d('conv2', self.o1, 16, 4, 16, 2, activation=self.activation)
self.w3, self.b3, self.o3 = layers.fc('fc3', layers.flatten(self.o2), 20, activation=self.activation)
self.ox = self.o3
elif self.arch == 'NIPS':
self.w1, self.b1, self.o1 = layers.conv2d('conv1', self.input_ph, 16, 8, self.input_channels, 4, activation=self.activation)
self.w2, self.b2, self.o2 = layers.conv2d('conv2', self.o1, 32, 4, 16, 2, activation=self.activation)
self.w3, self.b3, self.o3 = layers.fc('fc3', layers.flatten(self.o2), 256, activation=self.activation)
self.ox = self.o3
elif self.arch == 'NATURE':
self.w1, self.b1, self.o1 = layers.conv2d('conv1', self.input_ph, 32, 8, self.input_channels, 4, activation=self.activation)
self.w2, self.b2, self.o2 = layers.conv2d('conv2', self.o1, 64, 4, 32, 2, activation=self.activation)
self.w3, self.b3, self.o3 = layers.conv2d('conv3', self.o2, 64, 3, 64, 1, activation=self.activation)
self.w4, self.b4, self.o4 = layers.fc('fc4', layers.flatten(self.o3), 512, activation=self.activation)
self.ox = self.o4
else:
raise Exception('Invalid architecture `{}`'.format(self.arch))
if self.use_recurrent:
with tf.variable_scope('lstm_layer') as vs:
self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(
self.hidden_state_size, state_is_tuple=True, forget_bias=1.0)
batch_size = tf.shape(self.step_size)[0]
self.ox_reshaped = tf.reshape(self.ox,
[batch_size, -1, self.ox.get_shape().as_list()[-1]])
state_tuple = tf.contrib.rnn.LSTMStateTuple(
*tf.split(self.initial_lstm_state, 2, 1))
self.lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(
self.lstm_cell,
self.ox_reshaped,
initial_state=state_tuple,
sequence_length=self.step_size,
time_major=False)
self.lstm_state = tf.concat(self.lstm_state, 1)
self.ox = tf.reshape(self.lstm_outputs, [-1,self.hidden_state_size], name='reshaped_lstm_outputs')
# Get all LSTM trainable params
self.lstm_trainable_variables = [v for v in
tf.trainable_variables() if v.name.startswith(vs.name)]
return self.ox
