我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用layers.conv2d()。
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()
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()
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
