TensorFlow Get variables & print weights and Other methods

TensorFlow Get variables & print weights and Other methods

In the use of tensorflow, we often need to get the value of a variable, such as: print the weight of a layer, usually we can directly use the variable's Name property to get, but when we use some third party library to construct neural network layer, There is a situation where we cannot define variables of this layer ourselves because they are automatically defined. For example, when using TensorFlow's slim library:

def resnet_stack (images, Output_shape, Hparams, Scope=none): "" "Create a resnet style transfer block.  Args:images: [Batch-size, height, width, channels] image tensor to feed as input output_shape:output image shape In form [height, width, channels] hparams:hparams objects scope:variable scope returns:images after pro
  Cessing with resnet blocks. "" "end_points = {} if Hparams.noise_channel: # Separate the noise for visualization end_points[' noise '] = ima ges[:,:,:,-1] assert images.shape.as_list () [1:3] = = Output_shape[0:2] with Tf.variable_scope (scope, ' Resnet_style_ Transfer ', [images]): With Slim.arg_scope ([slim.conv2d], Normalizer_fn=slim.batch_norm, Kerne L_size=[hparams.generator_kernel_size] * 2, stride=1): NET = slim.conv2d (images, Hparam S.resnet_filters, Normalizer_fn=none, Activation_fn=tf.nn.relu) for blocks in range (Hparams.resne T_blocks): NET = Resnet_block (NET, hparams) end_points[' resnet_block_{} '. Format (block) = Net net = Slim.con v2d (NET, output_shape[-1], kernel_size=[1, 1], Normalizer_fn=none, ACTI Vation_fn=tf.nn.tanh, scope= ' conv_out ') end_points[' transferred_images '] = net return NET, end_points

We want to get the weight weight of the first convolution layer.
In training, these can be TensorFlow stored in tf.trainable_variables (), so we can print tf.trainable_variables () To get the name of the convolution layer (or you can also view the variable's name according to scope), and then use Tf.get_default_grap (). Get_tensor_by_name to get the variable. For a simple example:

Import TensorFlow as TF with
tf.variable_scope ("Generate"): With
    tf.variable_scope ("Resnet_stack"):
        # For simplicity's sake, there is no Third-party library to illustrate,
        bias = tf. Variable (0.0,name= "bias")
        weight = tf. Variable (0.0,name= "weight") for

TV in Tf.trainable_variables ():
    print (tv.name)

B = tf.get_default_graph (). Get_tensor_by_name ("generate/resnet_stack/bias:0")
W = tf.get_default_graph (). Get_tensor_by_name (" Generate/resnet_stack/weight:0 ") with

TF. Session () as Sess:
    Tf.global_variables_initializer (). Run ()
    print (Sess.run (b))
    Print (Sess.run (w))

The results are as follows: