Question

在训练cnn模型后，我想要显示重量或打印出重量，我该怎么办？我甚至无法在训练后打印出变量。谢谢！

Answer 1

要显示权重，您可以使用tf.image_summary()操作将卷积过滤器（或过滤器的一部分）转换为摘要原型，使用tf.train.SummaryWriter将其写入日志，并且使用TensorBoard可视化日志。

我们假设您有以下（简化）计划：

filter = tf.Variable(tf.truncated_normal([8, 8, 3]))
images = tf.placeholder(tf.float32, shape=[None, 28, 28])

conv = tf.nn.conv2d(images, filter, strides=[1, 1, 1, 1], padding="SAME")

# More ops...
loss = ...
optimizer = tf.GradientDescentOptimizer(0.01)
train_op = optimizer.minimize(loss)

filter_summary = tf.image_summary(filter)

sess = tf.Session()
summary_writer = tf.train.SummaryWriter('/tmp/logs', sess.graph_def)
for i in range(10000):
  sess.run(train_op)
  if i % 10 == 0:
    # Log a summary every 10 steps.
    summary_writer.add_summary(filter_summary, i)

执行此操作后，您可以启动TensorBoard以显示/tmp/logs中的日志，您将能够看到过滤器的可视化。

请注意，此技巧可将深度3滤镜显示为RGB图像（以匹配输入图像的通道）。如果您有更深层次的过滤器，或者将它们理解为颜色通道没有意义，您可以使用tf.split() op在深度维度上拆分过滤器，并为每个深度生成一个图像摘要。

Answer 2

就像@mrry所说，你可以使用tf.image_summary。例如，对于cifar10_train.py，您可以将此代码放在def train()下的某个位置。请注意如何访问范围'conv1'

下的var

# Visualize conv1 features
with tf.variable_scope('conv1') as scope_conv:
  weights = tf.get_variable('weights')

  # scale weights to [0 255] and convert to uint8 (maybe change scaling?)
  x_min = tf.reduce_min(weights)
  x_max = tf.reduce_max(weights)
  weights_0_to_1 = (weights - x_min) / (x_max - x_min)
  weights_0_to_255_uint8 = tf.image.convert_image_dtype (weights_0_to_1, dtype=tf.uint8)

  # to tf.image_summary format [batch_size, height, width, channels]
  weights_transposed = tf.transpose (weights_0_to_255_uint8, [3, 0, 1, 2])

  # this will display random 3 filters from the 64 in conv1
  tf.image_summary('conv1/filters', weights_transposed, max_images=3)

如果您想在一个漂亮的网格中可视化所有conv1过滤器，则必须自己将它们组织到网格中。我今天就这样做了，所以现在我想分享gist for visualizing conv1 as a grid

Answer 3

您可以通过以下方式将值提取为numpy数组：

with tf.variable_scope('conv1', reuse=True) as scope_conv:
    W_conv1 = tf.get_variable('weights', shape=[5, 5, 1, 32])
    weights = W_conv1.eval()
    with open("conv1.weights.npz", "w") as outfile:
        np.save(outfile, weights)

请注意，您必须调整范围（在我的情况下为'conv1'）和变量名称（在我的情况下为'weights'）。

然后归结为可视化numpy数组。如何可视化numpy数组的一个例子是

#!/usr/bin/env python

"""Visualize numpy arrays."""

import numpy as np
import scipy.misc

arr = np.load('conv1.weights.npb')

# Get each 5x5 filter from the 5x5x1x32 array
for filter_ in range(arr.shape[3]):
    # Get the 5x5x1 filter:
    extracted_filter = arr[:, :, :, filter_]

    # Get rid of the last dimension (hence get 5x5):
    extracted_filter = np.squeeze(extracted_filter)

    # display the filter (might be very small - you can resize the window)
    scipy.misc.imshow(extracted_filter)

Answer 4

使用 tensorflow 2 API，有几个选项：

使用 get_weights() 函数提取的权重。

weights_n = model.layers[n].get_weights()[0]

使用 numpy() 转换函数提取偏差。

bias_n = model.layers[n].bias.numpy()

如何在Tensorflow中可视化cnn中的权重（变量）？

4 个答案: