我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用tensorflow.assert_variables_initialized()。
def dense(x, num_units, nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' fully connected layer ''' name = get_name('dense', counters) with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', [int(x.get_shape()[ 1]), num_units], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0]) x_init = tf.matmul(x, V_norm) m_init, v_init = tf.nn.moments(x_init, [0]) scale_init = init_scale / tf.sqrt(v_init + 1e-10) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init * scale_init, trainable=True) x_init = tf.reshape( scale_init, [1, num_units]) * (x_init - tf.reshape(m_init, [1, num_units])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V, g, b = get_vars_maybe_avg(['V', 'g', 'b'], ema) tf.assert_variables_initialized([V, g, b]) # use weight normalization (Salimans & Kingma, 2016) x = tf.matmul(x, V) scaler = g / tf.sqrt(tf.reduce_sum(tf.square(V), [0])) x = tf.reshape(scaler, [1, num_units]) * \ x + tf.reshape(b, [1, num_units]) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def conv2d(x, num_filters, filter_size=[3, 3], stride=[1, 1], pad='SAME', nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' convolutional layer ''' name = get_name('conv2d', counters) with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', filter_size + [int(x.get_shape()[-1]), num_filters], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1, 2]) x_init = tf.nn.conv2d(x, V_norm, [1] + stride + [1], pad) m_init, v_init = tf.nn.moments(x_init, [0, 1, 2]) scale_init = init_scale / tf.sqrt(v_init + 1e-8) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init * scale_init, trainable=True) x_init = tf.reshape(scale_init, [ 1, 1, 1, num_filters]) * (x_init - tf.reshape(m_init, [1, 1, 1, num_filters])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V, g, b = get_vars_maybe_avg(['V', 'g', 'b'], ema) tf.assert_variables_initialized([V, g, b]) # use weight normalization (Salimans & Kingma, 2016) W = tf.reshape(g, [1, 1, 1, num_filters]) * \ tf.nn.l2_normalize(V, [0, 1, 2]) # calculate convolutional layer output x = tf.nn.bias_add(tf.nn.conv2d(x, W, [1] + stride + [1], pad), b) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def deconv(inp, name, filter_size, out_channels, stride=1, padding='SAME', nonlinearity=None, init_scale=1.0): """ Deconvolution layer. See `conv`""" with tf.variable_scope(name): strides = [1, stride, stride, 1] [N, H, W, in_channels] = inp.get_shape().as_list() if padding == 'SAME': target_shape = [N, H * stride, W * stride, out_channels] else: target_shape = [N, H * stride + filter_size[0] - 1, W * stride + filter_size[1] - 1, out_channels] target_shape = tf.constant(target_shape, dtype=tf.int32) if tf.GLOBAL['init']: V = get_variable('V', shape=filter_size + (out_channels, in_channels), dtype=tf.float32, initializer=tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1, 3]) out = tf.nn.conv2d_transpose(inp, V_norm, target_shape, strides, padding) m_init, v_init = tf.nn.moments(out, [0, 1, 2]) scale_init = init_scale / tf.sqrt(v_init + 1e-8) g = get_variable('g', shape=None, dtype=tf.float32, initializer=scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg'])) b = get_variable('b', shape=None, dtype=tf.float32, initializer=-m_init * scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg'])) out = tf.reshape(scale_init, [1, 1, 1, out_channels]) * (out - tf.reshape(m_init, [1, 1, 1, out_channels])) if nonlinearity is not None: out = nonlinearity(out) else: V, g, b = get_variable('V'), get_variable('g'), get_variable('b') tf.assert_variables_initialized([V, g, b]) W = g[None, None, :, None] * tf.nn.l2_normalize(V, [0, 1, 3]) out = tf.nn.conv2d_transpose(inp, W, target_shape, strides, padding) + b[None, None, None] if nonlinearity is not None: out = nonlinearity(out) return out
def dense(x, num_units, nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' fully connected layer ''' name = get_name('dense', counters) with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', [int(x.get_shape()[1]),num_units], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0]) x_init = tf.matmul(x, V_norm) m_init, v_init = tf.nn.moments(x_init, [0]) scale_init = init_scale/tf.sqrt(v_init + 1e-10) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init*scale_init, trainable=True) x_init = tf.reshape(scale_init,[1,num_units])*(x_init-tf.reshape(m_init,[1,num_units])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V,g,b = get_vars_maybe_avg(['V','g','b'], ema) tf.assert_variables_initialized([V,g,b]) # use weight normalization (Salimans & Kingma, 2016) x = tf.matmul(x, V) scaler = g/tf.sqrt(tf.reduce_sum(tf.square(V),[0])) x = tf.reshape(scaler,[1,num_units])*x + tf.reshape(b,[1,num_units]) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def conv2d(x, num_filters, filter_size=[3,3], stride=[1,1], pad='SAME', nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' convolutional layer ''' name = get_name('conv2d', counters) with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', filter_size+[int(x.get_shape()[-1]),num_filters], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0,1,2]) x_init = tf.nn.conv2d(x, V_norm, [1]+stride+[1], pad) m_init, v_init = tf.nn.moments(x_init, [0,1,2]) scale_init = init_scale/tf.sqrt(v_init + 1e-8) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init*scale_init, trainable=True) x_init = tf.reshape(scale_init,[1,1,1,num_filters])*(x_init-tf.reshape(m_init,[1,1,1,num_filters])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V, g, b = get_vars_maybe_avg(['V', 'g', 'b'], ema) tf.assert_variables_initialized([V,g,b]) # use weight normalization (Salimans & Kingma, 2016) W = tf.reshape(g,[1,1,1,num_filters])*tf.nn.l2_normalize(V,[0,1,2]) # calculate convolutional layer output x = tf.nn.bias_add(tf.nn.conv2d(x, W, [1]+stride+[1], pad), b) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def deconv2d(x, num_filters, filter_size=[3,3], stride=[1,1], pad='SAME', nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' transposed convolutional layer ''' name = get_name('deconv2d', counters) xs = int_shape(x) if pad=='SAME': target_shape = [xs[0], xs[1]*stride[0], xs[2]*stride[1], num_filters] else: target_shape = [xs[0], xs[1]*stride[0] + filter_size[0]-1, xs[2]*stride[1] + filter_size[1]-1, num_filters] with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', filter_size+[num_filters,int(x.get_shape()[-1])], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0,1,3]) x_init = tf.nn.conv2d_transpose(x, V_norm, target_shape, [1]+stride+[1], padding=pad) m_init, v_init = tf.nn.moments(x_init, [0,1,2]) scale_init = init_scale/tf.sqrt(v_init + 1e-8) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init*scale_init, trainable=True) x_init = tf.reshape(scale_init,[1,1,1,num_filters])*(x_init-tf.reshape(m_init,[1,1,1,num_filters])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V, g, b = get_vars_maybe_avg(['V', 'g', 'b'], ema) tf.assert_variables_initialized([V,g,b]) # use weight normalization (Salimans & Kingma, 2016) W = tf.reshape(g,[1,1,num_filters,1])*tf.nn.l2_normalize(V,[0,1,3]) # calculate convolutional layer output x = tf.nn.conv2d_transpose(x, W, target_shape, [1]+stride+[1], padding=pad) x = tf.nn.bias_add(x, b) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def deconv2d(x, num_filters, filter_size=[3, 3], stride=[1, 1], pad='SAME', nonlinearity=None, init_scale=1., counters={}, init=False, ema=None, **kwargs): ''' transposed convolutional layer ''' name = get_name('deconv2d', counters) xs = int_shape(x) if pad == 'SAME': target_shape = [xs[0], xs[1] * stride[0], xs[2] * stride[1], num_filters] else: target_shape = [xs[0], xs[1] * stride[0] + filter_size[0] - 1, xs[2] * stride[1] + filter_size[1] - 1, num_filters] with tf.variable_scope(name): if init: # data based initialization of parameters V = tf.get_variable('V', filter_size + [num_filters, int(x.get_shape( )[-1])], tf.float32, tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1, 3]) x_init = tf.nn.conv2d_transpose(x, V_norm, target_shape, [ 1] + stride + [1], padding=pad) m_init, v_init = tf.nn.moments(x_init, [0, 1, 2]) scale_init = init_scale / tf.sqrt(v_init + 1e-8) g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init, trainable=True) b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init * scale_init, trainable=True) x_init = tf.reshape(scale_init, [ 1, 1, 1, num_filters]) * (x_init - tf.reshape(m_init, [1, 1, 1, num_filters])) if nonlinearity is not None: x_init = nonlinearity(x_init) return x_init else: V, g, b = get_vars_maybe_avg(['V', 'g', 'b'], ema) tf.assert_variables_initialized([V, g, b]) # use weight normalization (Salimans & Kingma, 2016) W = tf.reshape(g, [1, 1, num_filters, 1]) * \ tf.nn.l2_normalize(V, [0, 1, 3]) # calculate convolutional layer output x = tf.nn.conv2d_transpose( x, W, target_shape, [1] + stride + [1], padding=pad) x = tf.nn.bias_add(x, b) # apply nonlinearity if nonlinearity is not None: x = nonlinearity(x) return x
def conv(inp, name, filter_size, out_channels, stride=1, padding='SAME', nonlinearity=None, init_scale=1.0, dilation=None): """Convolutional layer. If tf.GLOBAL['init'] is true, this creates the layers paramenters (g, b, W) : L(x) = g|W| (*) x + b Args: x: input tensor name (str): variable scope name filter_size (int pair): filter size out_channels (int): number of output channels strid (int): horizontal and vertical stride padding (str): padding mode nonlinearity (func): activation function init_scale: initial scale for the weights and bias variables dilation: optional dilation rate """ with tf.variable_scope(name): strides = [1, stride, stride, 1] in_channels = inp.get_shape().as_list()[3] if tf.GLOBAL['init']: V = get_variable('V', shape=tuple(filter_size) + (in_channels, out_channels), dtype=tf.float32, initializer=tf.random_normal_initializer(0, 0.05), trainable=True) V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1, 2]) if dilation is None: out = tf.nn.conv2d(inp, V_norm, strides, padding) else: assert(stride == 1) out = tf.nn.atrous_conv2d(inp, V_norm, dilation, padding) m_init, v_init = tf.nn.moments(out, [0, 1, 2]) scale_init = init_scale / tf.sqrt(v_init + 1e-8) g = get_variable('g', shape=None, dtype=tf.float32, initializer=scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg'])) b = get_variable('b', shape=None, dtype=tf.float32, initializer=-m_init * scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg'])) out = tf.reshape(scale_init, [1, 1, 1, out_channels]) * (out - tf.reshape(m_init, [1, 1, 1, out_channels])) if nonlinearity is not None: out = nonlinearity(out) else: V, g, b = get_variable('V'), get_variable('g'), get_variable('b') tf.assert_variables_initialized([V, g, b]) W = g[None, None, None] * tf.nn.l2_normalize(V, [0, 1, 2]) if dilation is None: out = tf.nn.conv2d(inp, W, strides, padding) + b[None, None, None] else: assert(stride == 1) out = tf.nn.atrous_conv2d(inp, W, dilation, padding) + b[None, None, None] if nonlinearity is not None: out = nonlinearity(out) return out
def __init__(self, session, optimizer, actor_network, critic_network, state_dim, num_actions, init_exp=0.1, # initial exploration prob final_exp=0.0, # final exploration prob anneal_steps=1000, # N steps for annealing exploration discount_factor=0.99, # discount future rewards reg_param=0.001, # regularization constants max_gradient=5, # max gradient norms summary_writer=None, summary_every=100): # tensorflow machinery self.session = session self.optimizer = optimizer self.summary_writer = summary_writer # model components self.actor_network = actor_network self.critic_network = critic_network # training parameters self.state_dim = state_dim self.num_actions = num_actions self.discount_factor = discount_factor self.max_gradient = max_gradient self.reg_param = reg_param # exploration parameters self.exploration = init_exp self.init_exp = init_exp self.final_exp = final_exp self.anneal_steps = anneal_steps # counters self.train_iteration = 0 # rollout buffer self.state_buffer = [] self.reward_buffer = [] self.action_buffer = [] # create and initialize variables self.create_variables() var_lists = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) self.session.run(tf.variables_initializer(var_lists)) # make sure all variables are initialized self.session.run(tf.assert_variables_initialized()) if self.summary_writer is not None: # graph was not available when journalist was created self.summary_writer.add_graph(self.session.graph) self.summary_every = summary_every
def __init__(self, session, optimizer, policy_network, state_dim, num_actions, init_exp=0.5, # initial exploration prob final_exp=0.0, # final exploration prob anneal_steps=10000, # N steps for annealing exploration discount_factor=0.99, # discount future rewards reg_param=0.001, # regularization constants max_gradient=5, # max gradient norms summary_writer=None, summary_every=100): # tensorflow machinery self.session = session self.optimizer = optimizer self.summary_writer = summary_writer # model components self.policy_network = policy_network # training parameters self.state_dim = state_dim self.num_actions = num_actions self.discount_factor = discount_factor self.max_gradient = max_gradient self.reg_param = reg_param # exploration parameters self.exploration = init_exp self.init_exp = init_exp self.final_exp = final_exp self.anneal_steps = anneal_steps # counters self.train_iteration = 0 # rollout buffer self.state_buffer = [] self.reward_buffer = [] self.action_buffer = [] # record reward history for normalization self.all_rewards = [] self.max_reward_length = 1000000 # create and initialize variables self.create_variables() var_lists = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) self.session.run(tf.variables_initializer(var_lists)) # make sure all variables are initialized self.session.run(tf.assert_variables_initialized()) if self.summary_writer is not None: # graph was not available when journalist was created self.summary_writer.add_graph(self.session.graph) self.summary_every = summary_every
def __init__(self, session, optimizer, actor_network, critic_network, state_dim, action_dim, batch_size=32, replay_buffer_size=1000000, # size of replay buffer store_replay_every=1, # how frequent to store experience discount_factor=0.99, # discount future rewards target_update_rate=0.01, reg_param=0.01, # regularization constants max_gradient=5, # max gradient norms noise_sigma=0.20, noise_theta=0.15, summary_writer=None, summary_every=100): # tensorflow machinery self.session = session self.optimizer = optimizer self.summary_writer = summary_writer # model components self.actor_network = actor_network self.critic_network = critic_network self.replay_buffer = ReplayBuffer(buffer_size=replay_buffer_size) # training parameters self.batch_size = batch_size self.state_dim = state_dim self.action_dim = action_dim self.discount_factor = discount_factor self.target_update_rate = target_update_rate self.max_gradient = max_gradient self.reg_param = reg_param # Ornstein-Uhlenbeck noise for exploration self.noise_var = tf.Variable(tf.zeros([1, action_dim])) noise_random = tf.random_normal([1, action_dim], stddev=noise_sigma) self.noise = self.noise_var.assign_sub((noise_theta) * self.noise_var - noise_random) # counters self.store_replay_every = store_replay_every self.store_experience_cnt = 0 self.train_iteration = 0 # create and initialize variables self.create_variables() var_lists = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) self.session.run(tf.variables_initializer(var_lists)) # make sure all variables are initialized self.session.run(tf.assert_variables_initialized()) if self.summary_writer is not None: # graph was not available when journalist was created self.summary_writer.add_graph(self.session.graph) self.summary_every = summary_every
def __init__(self, session, optimizer, q_network, state_dim, num_actions, batch_size=32, init_exp=0.5, # initial exploration prob final_exp=0.1, # final exploration prob anneal_steps=10000, # N steps for annealing exploration replay_buffer_size=10000, store_replay_every=5, # how frequent to store experience discount_factor=0.9, # discount future rewards target_update_rate=0.01, reg_param=0.01, # regularization constants max_gradient=5, # max gradient norms double_q_learning=False, summary_writer=None, summary_every=100): # tensorflow machinery self.session = session self.optimizer = optimizer self.summary_writer = summary_writer # model components self.q_network = q_network self.replay_buffer = ReplayBuffer(buffer_size=replay_buffer_size) # Q learning parameters self.batch_size = batch_size self.state_dim = state_dim self.num_actions = num_actions self.exploration = init_exp self.init_exp = init_exp self.final_exp = final_exp self.anneal_steps = anneal_steps self.discount_factor = discount_factor self.target_update_rate = target_update_rate self.double_q_learning = double_q_learning # training parameters self.max_gradient = max_gradient self.reg_param = reg_param # counters self.store_replay_every = store_replay_every self.store_experience_cnt = 0 self.train_iteration = 0 # create and initialize variables self.create_variables() var_lists = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) self.session.run(tf.variables_initializer(var_lists)) # make sure all variables are initialized self.session.run(tf.assert_variables_initialized()) if self.summary_writer is not None: # graph was not available when journalist was created self.summary_writer.add_graph(self.session.graph) self.summary_every = summary_every
