在Google协作中加载张sorboard时出错

Question

我在Google Colab中面临tensorboard加载问题。我试着卸载，然后再安装，但没有成功。我在分享代码和错误。

!pip install tensorboard

%load_ext tensorboard

log_folder = 'log1'

callbacks = TensorBoard(log_dir= log_folder, histogram_freq= 1)

model.fit(train_X, train_y, validation_data = (test_X, test_y),callbacks= callbacks,verbose= 0, epochs = 20)

%tensorboard --logdir = '/content/log1'  I tried withour quotes as well i.e /content/log1

输入图片描述

我尝试加载tensorboard并尝试卸载然后重新安装

Answer 1

"在--logdir和<PATH>之间似乎是这里的问题。比如--logdir log1

Answer 2

我修复了这个错误。当我删除= it worked

时，赋值运算符产生了问题。%tensorboard—logdir '/content/log1'

Answer 3

尝试创建对日志记录和累积函数所需的目标目录的访问。节省CPU内存的一种方法是使用磁盘空间，这就是为什么汇总函数在更新后工作。

示例:带有更新频率的回调函数和图标示例。

import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
from datetime import datetime
import matplotlib.pyplot as plt
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\datasets\downloads\Actors\train\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\datasets\downloads\Actors\train\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
list_image_greyscales = []
for file in files.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_image_greyscales.append(tf.image.rgb_to_grayscale(image[:,:,0:3]))
list_label.append(1)

for file in files_2.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_image_greyscales.append(tf.image.rgb_to_grayscale(image[:,:,0:3]))
list_label.append(9)

checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
log_dir = os.path.dirname(checkpoint_path)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices((tf.constant(tf.cast(list_file, dtype=tf.int64), shape=(10, 1, 32, 32, 4), dtype=tf.int64),tf.constant(list_label, shape=(10, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
tb_callback = tf.keras.callbacks.TensorBoard(log_dir, update_freq=1, histogram_freq=1)
class custom_callback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if( logs['accuracy'] >= 0.95 ):
self.model.stop_training = True

custom_callback = custom_callback()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(192, activation='relu'),
tf.keras.layers.Dense(10),
])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
# Creates a file writer for the log directory.
file_writer = tf.summary.create_file_writer(log_dir  + "\" + datetime.now().strftime("%Y%m%d-%H%M%S") )
# Using the file writer, log the reshaped image.
with file_writer.as_default():
for i in range(10):
tf.summary.image("Training data", tf.constant( list_image_greyscales[i], shape=(1,32,32,1) ), step=i)
tf.summary.scalar("Training data label", data=float(i), step=i)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=10, callbacks=[tb_callback, custom_callback] )
model.save_weights(checkpoint_path)
plt.figure(figsize=(5,2))
plt.title("Actors recognitions")
for i in range(len(list_file)):
img = tf.keras.preprocessing.image.array_to_img(
list_file[i],
data_format=None,
scale=True
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
plt.subplot(5, 2, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(list_file_actual[i])
plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" +  str(list_label_actual[tf.math.argmax(score)]))

plt.show()
input('...')
### tensorboard --logdir="F:modelscheckpointtest_tf_tensorboard_2\"

输出:Tensorboard训练和显著数据。