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python - SummaryWriter 不在 TensorFlow 中输出图形

转载 作者：太空宇宙 更新时间：2023-11-04 05:12:37 24 4
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我正在尝试使用 tensorboard 通过 summaryWriter 分析 tensorflow 中的图形。但是，TensorFlow 不会输出包含信息的“图形”文件夹。也许我错过了一个命令或者它不在正确的位置？ 

writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph());

是我用的。我认为这可能不再适用于 TensorFlow 1.0(只是 summarywriter 命令)

import numpy as np
import tensorflow as tf
# %matplotlib inline
import matplotlib.pyplot as plt

# Global config variables
num_steps = 5 # number of truncated backprop steps ('n' in the discussion above)
batch_size = 200
num_classes = 2
state_size = 4
learning_rate = 0.1
logs_path = "./graph"

def gen_data(size=1000000):
    X = np.array(np.random.choice(2, size=(size,)))
    Y = []
    for i in range(size):
        threshold = 0.5
        if X[i-3] == 1:
            threshold += 0.5
        if X[i-8] == 1:
            threshold -= 0.25
        if np.random.rand() > threshold:
            Y.append(0)
        else:
            Y.append(1)
    return X, np.array(Y)

# adapted from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/rnn/ptb/reader.py
def gen_batch(raw_data, batch_size, num_steps):
    raw_x, raw_y = raw_data
    data_length = len(raw_x)

    # partition raw data into batches and stack them vertically in a data matrix
    batch_partition_length = data_length // batch_size
    data_x = np.zeros([batch_size, batch_partition_length], dtype=np.int32)
    data_y = np.zeros([batch_size, batch_partition_length], dtype=np.int32)
    for i in range(batch_size):
        data_x[i] = raw_x[batch_partition_length * i:batch_partition_length * (i + 1)]
        data_y[i] = raw_y[batch_partition_length * i:batch_partition_length * (i + 1)]
    # further divide batch partitions into num_steps for truncated backprop
    epoch_size = batch_partition_length // num_steps

    for i in range(epoch_size):
        x = data_x[:, i * num_steps:(i + 1) * num_steps]
        y = data_y[:, i * num_steps:(i + 1) * num_steps]
        yield (x, y)

def gen_epochs(n, num_steps):
    for i in range(n):
        yield gen_batch(gen_data(), batch_size, num_steps)

"""
Placeholders
"""

x = tf.placeholder(tf.int32, [batch_size, num_steps], name='input_placeholder')
y = tf.placeholder(tf.int32, [batch_size, num_steps], name='labels_placeholder')
init_state = tf.zeros([batch_size, state_size])


"""
Inputs
"""

x_one_hot = tf.one_hot(x, num_classes)
rnn_inputs = tf.unstack(x_one_hot, axis=1)


"""
RNN
"""

cell = tf.contrib.rnn.BasicRNNCell(state_size)
rnn_outputs, final_state =  tf.contrib.rnn.static_rnn(cell, rnn_inputs, initial_state=init_state)



"""
Predictions, loss, training step
"""

with tf.variable_scope('softmax'):
    W = tf.get_variable('W', [state_size, num_classes])
    b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(0.0))
logits = [tf.matmul(rnn_output, W) + b for rnn_output in rnn_outputs]
predictions = [tf.nn.softmax(logit) for logit in logits]

y_as_list = [tf.squeeze(i, axis=[1]) for i in tf.split(axis=1, num_or_size_splits=num_steps, value=y)]

loss_weights = [tf.ones([batch_size]) for i in range(num_steps)]
losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, y_as_list, loss_weights)
tf.scalar_summary("losses", losses)
total_loss = tf.reduce_mean(losses)
train_step = tf.train.AdagradOptimizer(learning_rate).minimize(total_loss)


# Not sure why this is not outputting a graph for tensorboard
writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph());


"""
Function to train the network
"""

def train_network(num_epochs, num_steps, state_size=4, verbose=True):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        training_losses = []
        saved = gen_epochs(num_epochs, num_steps);

        for idx, epoch in enumerate(gen_epochs(num_epochs, num_steps)):
            training_loss = 0
            training_state = np.zeros((batch_size, state_size))
            if verbose:
                print("\nEPOCH", idx)
            for step, (X, Y) in enumerate(epoch):
                tr_losses, training_loss_, training_state, _ = \
                    sess.run([losses,
                              total_loss,
                              final_state,
                              train_step],
                                  feed_dict={x:X, y:Y, init_state:training_state})
                training_loss += training_loss_
                if step % 100 == 0 and step > 0:
                    if verbose:
                        print("Average loss at step", step,
                              "for last 250 steps:", training_loss/100)
                    training_losses.append(training_loss/100)
                    training_loss = 0

    return training_losses

training_losses = train_network(1,num_steps)
plt.plot(training_losses)


# tensorboard --logdir="my_graph"

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