如何使用 python 在 Tensorboard 上显示模型的权重和偏差

我创建了以下模型进行训练，并希望在 Tensorboard 上将其可视化：

## Basic Cell LSTM tensorflow

index_in_epoch = 0;
perm_array  = np.arange(x_train.shape[0])
np.random.shuffle(perm_array)

# function to get the next batch
def get_next_batch(batch_size):
    global index_in_epoch, x_train, perm_array   
    start = index_in_epoch
    index_in_epoch += batch_size

    if index_in_epoch > x_train.shape[0]:
        np.random.shuffle(perm_array) # shuffle permutation array
        start = 0 # start next epoch
        index_in_epoch = batch_size

    end = index_in_epoch
    return x_train[perm_array[start:end]], y_train[perm_array[start:end]]

# parameters
n_steps = seq_len-1 
n_inputs = 4 
n_neurons = 200 
n_outputs = 4
n_layers = 2
learning_rate = 0.001
batch_size = 50
n_epochs = 100 
train_set_size = x_train.shape[0]
test_set_size = x_test.shape[0]

tf.reset_default_graph()

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_outputs])

# use LSTM Cell with peephole connections
layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons, 
                                  activation=tf.nn.leaky_relu, use_peepholes = True)
          for layer in range(n_layers)]

multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)

stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons]) 
stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)
outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])
outputs = outputs[:,n_steps-1,:] # keep only last output of sequence

loss = tf.reduce_mean(tf.square(outputs - y)) # loss function = mean squared error 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) 
training_op = optimizer.minimize(loss)

# run graph
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())
    for iteration in range(int(n_epochs*train_set_size/batch_size)):
        x_batch, y_batch = get_next_batch(batch_size) # fetch the next training batch 
        sess.run(training_op, feed_dict={X: x_batch, y: y_batch}) 
        if iteration % int(5*train_set_size/batch_size) == 0:
            mse_train = loss.eval(feed_dict={X: x_train, y: y_train}) 
            mse_valid = loss.eval(feed_dict={X: x_valid, y: y_valid}) 
            print('%.2f epochs: MSE train/valid = %.6f/%.6f'%(
                iteration*batch_size/train_set_size, mse_train, mse_valid))

我想知道如何才能看到权重和偏差以及我为训练提供的输入之间的相关性。

请帮助我。如果我的问题没有答案，请告诉我是否有任何建议。请询问我是否需要任何东西，我会得到并告诉您。

我认为在 Tensorboard 上可视化权重的最简单方法是将它们绘制为直方图。例如，您可以按如下方式记录图层。

for i, layer in enumerate(layers):
    tf.summary.histogram('layer{0}'.format(i), layer)

为要记录的每个层或变量创建摘要后，您必须使用 merge_all 函数收集所有它们并创建一个 FileWriter。

merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('directory_name', sess.graph)

最后，您必须与其他操作一起运行摘要并将结果添加到您的编写器中。

summary, _ = sess.run([merged, training_op], feed_dict={X: x_batch, y: y_batch})
writer.add_summary(summary, iteration_number)

如果您想对权重进行进一步分析，我建议将它们恢复为 numpy 数组，如下所述here https://stackoverflow.com/a/36193677.

不过，我不知道有什么简单的方法可以在 Tensorboard 上绘制相关性。如果您只想获得输入的相关性，我建议使用 scikit 甚至 pandas （.corr 函数 https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html）如果你的数据集不是很大。

我希望这有帮助。你也可以参考这个tutorial https://www.datacamp.com/community/tutorials/tensorboard-tutorial以获得更深入的解释。