我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用tensorflow.histogram_summary()。
def conv_max_pool_2x2(x, conv_width, conv_height, in_depth, out_depth, name="conv"): with tf.name_scope(name) as scope: W_conv = weight_variable([conv_width, conv_height, in_depth, out_depth]) b_conv = bias_variable([out_depth]) h_conv = tf.nn.relu(conv2d(x, W_conv) + b_conv) h_pool = max_pool_2x2(h_conv) with tf.name_scope("summaries") as scope: # TIPS: to display the 32 convolution filters, re-arrange the # weigths to look like 32 images with a transposition. a = tf.reshape(W_conv, [conv_width * conv_height * in_depth, out_depth]) b = tf.transpose(a) c = tf.reshape(b, [out_depth, conv_width, conv_height * in_depth, 1]) conv_image = tf.image_summary(name + " filter", c, out_depth) # TIPS: by looking at the weights histogram, we can see the the # weigths are explosing or vanishing. W_conv_hist = tf.histogram_summary(name + " weights", W_conv) b_conv_hist = tf.histogram_summary(name + " biases", b_conv) return h_pool
def build_encoder(self): """Inference Network. q(h|X)""" with tf.variable_scope("encoder"): q_cell = tf.nn.rnn_cell.LSTMCell(self.embed_dim, self.vocab_size) a_cell = tf.nn.rnn_cell.LSTMCell(self.embed_dim, self.vocab_size) l1 = tf.nn.relu(tf.nn.rnn_cell.linear(tf.expand_dims(self.x, 0), self.embed_dim, bias=True, scope="l1")) l2 = tf.nn.relu(tf.nn.rnn_cell.linear(l1, self.embed_dim, bias=True, scope="l2")) self.mu = tf.nn.rnn_cell.linear(l2, self.h_dim, bias=True, scope="mu") self.log_sigma_sq = tf.nn.rnn_cell.linear(l2, self.h_dim, bias=True, scope="log_sigma_sq") eps = tf.random_normal((1, self.h_dim), 0, 1, dtype=tf.float32) sigma = tf.sqrt(tf.exp(self.log_sigma_sq)) _ = tf.histogram_summary("mu", self.mu) _ = tf.histogram_summary("sigma", sigma) self.h = self.mu + sigma * eps
def build_encoder(self): """Inference Network. q(h|X)""" with tf.variable_scope("encoder"): self.l1_lin = linear(tf.expand_dims(self.x, 0), self.embed_dim, bias=True, scope="l1") self.l1 = tf.nn.relu(self.l1_lin) self.l2_lin = linear(self.l1, self.embed_dim, bias=True, scope="l2") self.l2 = tf.nn.relu(self.l2_lin) self.mu = linear(self.l2, self.h_dim, bias=True, scope="mu") self.log_sigma_sq = linear(self.l2, self.h_dim, bias=True, scope="log_sigma_sq") self.eps = tf.random_normal((1, self.h_dim), 0, 1, dtype=tf.float32) self.sigma = tf.sqrt(tf.exp(self.log_sigma_sq)) self.h = tf.add(self.mu, tf.mul(self.sigma, self.eps)) _ = tf.histogram_summary("mu", self.mu) _ = tf.histogram_summary("sigma", self.sigma) _ = tf.histogram_summary("h", self.h) _ = tf.histogram_summary("mu + sigma", self.mu + self.sigma)
def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) # tf.histogram_summary(tensor_name + '/activations', x) tf.summary.histogram(tensor_name + '/activations', x) # tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def define_summaries(self): '''Helper function for init_opt''' all_sum = {'g': [], 'd': [], 'hr_g': [], 'hr_d': [], 'hist': []} for k, v in self.log_vars: if k.startswith('g'): all_sum['g'].append(tf.scalar_summary(k, v)) elif k.startswith('d'): all_sum['d'].append(tf.scalar_summary(k, v)) elif k.startswith('hr_g'): all_sum['hr_g'].append(tf.scalar_summary(k, v)) elif k.startswith('hr_d'): all_sum['hr_d'].append(tf.scalar_summary(k, v)) elif k.startswith('hist'): all_sum['hist'].append(tf.histogram_summary(k, v)) self.g_sum = tf.merge_summary(all_sum['g']) self.d_sum = tf.merge_summary(all_sum['d']) self.hr_g_sum = tf.merge_summary(all_sum['hr_g']) self.hr_d_sum = tf.merge_summary(all_sum['hr_d']) self.hist_sum = tf.merge_summary(all_sum['hist'])
def add_layers(inputs, in_size, out_size, layer_name, keep_prob, activation_function=None): # add one more layer and return the output of this layer weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) wx_plus_b = tf.matmul(inputs, weights) + biases # here to dropout # ? wx_plus_b ?drop????? # keep_prob ??????drop?????? sess.run ? feed wx_plus_b = tf.nn.dropout(wx_plus_b, keep_prob) if activation_function is None: outputs = wx_plus_b else: outputs = activation_function(wx_plus_b) tf.histogram_summary(layer_name + '/outputs', outputs) return outputs
def nn_layer_(self,input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): """Reusable code for making a simple neural net layer. It does a matrix multiply, bias add, and then uses relu to nonlinearize. It also sets up name scoping so that the resultant graph is easy to read, and adds a number of summary ops. """ # Adding a name scope ensures logical grouping of the layers in the graph. with tf.name_scope(layer_name): with tf.name_scope('weights'): weights = self.weight_variable([input_dim, output_dim]) self.variable_summaries(weights, layer_name + '/weights') with tf.name_scope('biases'): biases = self.bias_variable([output_dim]) self.variable_summaries(biases, layer_name + '/biases') with tf.name_scope('Wx_plus_b'): preactivate = tf.matmul(input_tensor, weights) + biases tf.histogram_summary(layer_name + '/pre_activations', preactivate) activations = act(preactivate, 'activation') tf.histogram_summary(layer_name + '/activations', activations) return activations
def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def _setup_training(self): """ Set up a data flow graph for fine tuning """ layer_num = self.layer_num act_func = ACTIVATE_FUNC[self.activate_func] sigma = self.sigma lr = self.learning_rate weights = self.weights biases = self.biases data1, data2 = self.data1, self.data2 batch_size = self.batch_size optimizer = OPTIMIZER[self.optimizer] with tf.name_scope("training"): s1 = self._obtain_score(data1, weights, biases, act_func, "1") s2 = self._obtain_score(data2, weights, biases, act_func, "2") with tf.name_scope("cost"): sum_cost = tf.reduce_sum(tf.log(1 + tf.exp(-sigma*(s1-s2)))) self.cost = cost = sum_cost / batch_size self.optimize = optimizer(lr).minimize(cost) for n in range(layer_num-1): tf.histogram_summary("weight"+str(n), weights[n]) tf.histogram_summary("bias"+str(n), biases[n]) tf.scalar_summary("cost", cost)
def _activation_summary(self, x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. # Error: these summaries cause high classifier error!!! # All inputs to node MergeSummary/MergeSummary must be from the same frame. # tensor_name = re.sub('%s_[0-9]*/' % "tower", '', x.op.name) # tf.histogram_summary(tensor_name + '/activations', x) # tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def Train(self, loss, learning_rate, clip_value_min, clip_value_max, name='training'): tf.scalar_summary(':'.join([name, loss.op.name]), loss) optimizer = tf.train.AdagradOptimizer(learning_rate) grads_and_vars = optimizer.compute_gradients(loss) clipped_grads_and_vars = [ (tf.clip_by_value(g, clip_value_min, clip_value_max), v) for g, v in grads_and_vars ] for g, v in clipped_grads_and_vars: _ = tf.histogram_summary(':'.join([name, v.name]), v) _ = tf.histogram_summary('%s: gradient for %s' % (name, v.name), g) train_op = optimizer.apply_gradients(clipped_grads_and_vars) return train_op
def _activation_summary(self, x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % 'tower', '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def fc_layer(self, bottom, name): with tf.variable_scope(name) as scope: shape = bottom.get_shape().as_list() dim = 1 for d in shape[1:]: dim *= d x = tf.reshape(bottom, [-1, dim]) with tf.device('/cpu:0'): weights = self.get_fc_weight(name) biases = self.get_fc_bias(name) # Fully connected layer. Note that the '+' operation automatically # broadcasts the biases. fc = tf.nn.bias_add(tf.matmul(x, weights), biases) #tf.histogram_summary('adascan/'+name+'_activations', fc) #tf.histogram_summary('adascan/'+name+'_weights', weights) scope.reuse_variables() return fc
def add_optimizer(self): self.global_step = tf.Variable(0, trainable=False) learning_rate = tf.train.exponential_decay(0.01, self.global_step, 50, 0.1, staircase=True) optimizer = tf.train.GradientDescentOptimizer(learning_rate) gradients = optimizer.compute_gradients(self.loss) self.apply_gradient_op = optimizer.apply_gradients(gradients, self.global_step) for var in tf.trainable_variables(): tf.histogram_summary(var.op.name, var) for grad, var in gradients: if grad is not None: tf.histogram_summary(var.op.name + '/gradients', grad) return self.apply_gradient_op
def add_conv_layer(self, scope_name, layer_input, filter_size, input_channels, output_channels, padding='SAME', should_init_wb=True): with tf.variable_scope(scope_name): weights_shape = filter_size + [input_channels, output_channels] initial_weights, initial_bias = self.__get_init_params(scope_name, should_init_wb) self.total_weights += weights_shape[0] * weights_shape[1] * weights_shape[2] * weights_shape[3] self.logger.info('Weight shape:{} for scope:{}'.format(weights_shape, tf.get_variable_scope().name)) conv_weights = self.__get_variable('weights', weights_shape, tf.float32, initializer=initial_weights) tf.scalar_summary(scope_name + '/weight_sparsity', tf.nn.zero_fraction(conv_weights)) tf.histogram_summary(scope_name + '/weights', conv_weights) conv = tf.nn.conv2d(layer_input, conv_weights, strides=[1, 1, 1, 1], padding=padding) conv_biases = self.__get_variable('biases', [output_channels], tf.float32, initializer=initial_bias) layer_output = tf.nn.relu(tf.nn.bias_add(conv, conv_biases)) return layer_output
def add_activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def add_summaries(summaries, learning_rate, grads): """ Add summary ops""" # Track quantities for Tensorboard display summaries.append(tf.scalar_summary('learning_rate', learning_rate)) # Add histograms for gradients. for grad, var in grads: if grad is not None: summaries.append( tf.histogram_summary(var.op.name + '/gradients', grad)) # Add histograms for trainable variables. for var in tf.trainable_variables(): summaries.append(tf.histogram_summary(var.op.name, var)) # Build the summary operation from the last tower summaries. summary_op = tf.merge_summary(summaries) return summary_op
def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = x.op.name # tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def _add_gradients_summaries(grads_and_vars): """Add histogram summaries to gradients. Note: The summaries are also added to the SUMMARIES collection. Args: grads_and_vars: A list of gradient to variable pairs (tuples). Returns: The _list_ of the added summaries for grads_and_vars. """ summaries = [] for grad, var in grads_and_vars: if grad is not None: if isinstance(grad, tf.IndexedSlices): grad_values = grad.values else: grad_values = grad summaries.append(tf.histogram_summary(var.op.name + ':gradient', grad_values)) summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm', tf.global_norm([grad_values]))) else: tf.logging.info('Var %s has no gradient', var.op.name) return summaries
def create_train_summaries(learning_rate, clas_loss, reg_loss, rpn_loss, clas_accuracy, clas_positive_percentage, clas_positive_accuracy, VGG16D_activations, clas_activations): with tf.name_scope('train'): learning_rate_summary = tf.scalar_summary('learning_rate', learning_rate) loss_clas_summary = tf.scalar_summary('loss/clas', clas_loss) loss_reg_summary = tf.scalar_summary('loss/reg', reg_loss) loss_rpn_summary = tf.scalar_summary('loss/rpn', rpn_loss) stat_accuracy_summary = tf.scalar_summary('stat/accuracy', clas_accuracy) stat_positive_percentage_summary = tf.scalar_summary('stat/positive_percentage', clas_positive_percentage) stat_positive_accuracy_summary = tf.scalar_summary('stat/positive_accuracy', clas_positive_accuracy) VGG16D_histogram = tf.histogram_summary('activations/VGG16D', VGG16D_activations) clas_histogram = tf.histogram_summary('activations/clas', clas_activations) return tf.merge_summary([learning_rate_summary, loss_clas_summary, loss_reg_summary, loss_rpn_summary, stat_accuracy_summary, stat_positive_percentage_summary, stat_positive_accuracy_summary, VGG16D_histogram, clas_histogram])
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): # add one more layer and return the output of this layer layer_name = 'layer%s' % n_layer with tf.variable_scope(layer_name): with tf.variable_scope('weights'): Weights = tf.get_variable(shape=[in_size, out_size], name='W', initializer=xavier_initializer()) tf.histogram_summary(layer_name + '/weights', Weights) with tf.variable_scope('biases'): biases = tf.get_variable(shape=[1, out_size], name='b', initializer=xavier_initializer()) tf.histogram_summary(layer_name + '/biases', biases) with tf.variable_scope('Wx_plus_b'): Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases) if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b, ) tf.histogram_summary(layer_name + '/outputs', outputs) return outputs # Make up some real data
def build_model(self): self.images = tf.placeholder(tf.float32, [self.batch_size] + [self.output_size, self.output_size, self.c_dim], name='real_images') self.sample_images= tf.placeholder(tf.float32, [self.sample_size] + [self.output_size, self.output_size, self.c_dim], name='sample_images') self.z = tf.placeholder(tf.float32, [None, self.z_dim], name='z') self.z_sum = tf.histogram_summary("z", self.z) self.G = self.generator(self.z) self.D, self.D_logits = self.discriminator(self.images) self.sampler = self.sampler(self.z) self.D_, self.D_logits_ = self.discriminator(self.G, reuse=True)
def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measures the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def variable_summaries(var, name): """ Attach a lot of summaries to a Tensor for Tensorboard visualization. Ref: https://www.tensorflow.org/versions/r0.11/how_tos/summaries_and_tensorboard/index.html :param var: Variable to summarize :param name: Summary name """ with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.scalar_summary('mean/' + name, mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.scalar_summary('stddev/' + name, stddev) tf.scalar_summary('max/' + name, tf.reduce_max(var)) tf.scalar_summary('min/' + name, tf.reduce_min(var)) tf.histogram_summary(name, var)
def setupSummary(self): self.WHist = tf.histogram_summary("%s/weights" % self.name, self.W) self.BHist = tf.histogram_summary("%s/biases" % self.name, self.b) self.outputHist = tf.histogram_summary("%s/output" % self.name, self.output)
def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measure the sparsity of activations. Args: x: Tensor """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
