教程|TensorEditor：一个小白都能快速玩转的神经网络搭建工具

消息来源:baojiabao.com 作者: 发布时间：2026-08-03

报价宝综合消息教程|TensorEditor：一个小白都能快速玩转的神经网络搭建工具

机器之心整理

参与：思源

近日，机器之心发现一个非常有意思的工具，可以用可视化的方式轻松添加卷积层、全连接层和池化层等层级，然后生成可执行的 TensorFlow 代码。此外，我们也尝试搭建一个简单的卷捷豹构，并在本地 TensorFlow 环境下测试生成的代码。

工具地址：https://www.tensoreditor.com/

TensorEditor 是一个强大的机器学习工具，甚至小白都能以可视化的方式快速生成整个模型的代码。通过 TensorEditor，小白可以连接卷积层、全连接层和池化层等可视化结点创建整个模型，且我们可以将它们转化为 TensorFlow 和 Python 代码，并进一步在自己的环境中运行。

基本上，TensorEditor 的步骤即定义我们的数据集、图像或特征，然后创建深度神经网络并下载 Python 2.7 的代码，最后就需要在我们自己的 TensorFLow 环境下运行就好了。

通过 TensorEditor，我们不仅可以创建深度网络并避免一些常见的代码问题，同时还能生成基于 TensorFlow Estimator 的高效代码。如下所示，机器之心尝试构建了一个简单的卷积网络，我们使用了两个卷积层、两个池化层和一个全连接层，并在最后的 Estimator 使用了交叉熵损失函数和 Adagrad 最优化方法。

上述简单搭建的卷积网络同样可以生成完全可执行的代码，这样可以避免大量的一般代码问题与重复性工作。

import
tensorflow as tf
import
pandas as pd
tf
.
logging
. set_verbosity ( tf . logging . INFO )
project_name
=
"CNN"
train_csv_file
=
""
test_csv_file
=
""
image_resize
=[
28
,
28
]
def model_fn
(
features
,
labels
,
mode
,
params
):
convolutional_2d_1
=
tf
.
layers
.
conv2d
(
inputs
=
features
,
filters
=
32
,
kernel_size
=[
3
,
3
],
strides
=(
1
,
1
),
padding
=
"same"
,
data_format
=
"channels_last"
,
dilation_rate
=(
1
,
1
),
activation
=
tf
.
nn
.
relu
,
use_bias
=
True
)
max_pool_2d_1
=
tf
.
layers
.
max_pooling2d
(
inputs
=
convolutional_2d_1
,
pool_size
=[
2
,
2
],
strides
=[
2
,
2
],
padding
=
"same"
,
data_format
=
"channels_last"
)
convolutional_2d_2
=
tf
.
layers
.
conv2d
(
inputs
=
max_pool_2d_1
,
filters
=
64
,
kernel_size
=[
3
,
3
],
strides
=(
1
,
1
),
padding
=
"same"
,
data_format
=
"channels_last"
,
dilation_rate
=(
1
,
1
),
activation
=
tf
.
nn
.
relu
,
use_bias
=
True
)
max_pool_2d_2
=
tf
.
layers
.
max_pooling2d
(
inputs
=
max_pool_2d_1
,
pool_size
=[
2
,
2
],
strides
=[
2
,
2
],
padding
=
"same"
,
data_format
=
"channels_last"
)
convolutional_2d_3
=
tf
.
layers
.
conv2d
(
inputs
=
max_pool_2d_2
,
filters
=
128
,
kernel_size
=[
3
,
3
],
strides
=(
1
,
1
),
padding
=
"same"
,
data_format
=
"channels_last"
,
dilation_rate
=(
1
,
1
),
activation
=
tf
.
nn
.
relu
,
use_bias
=
True
)
max_pool_2d_3
=
tf
.
layers
.
max_pooling2d
(
inputs
=
convolutional_2d_3
,
pool_size
=[
2
,
2
],
strides
=[
2
,
2
],
padding
=
"same"
,
data_format
=
"channels_last"
)
flatten_1
=
tf
.
reshape
(
max_pool_2d_3
,

[-
1
,

2048
])
dense_1
=
tf
.
layers
.
dense
(
inputs
=
flatten_1
,
units
=
1024
,
activation
=
tf
.
nn
.
relu
)
dropout_1
=
tf
.
layers
.
dropout
(
inputs
=
dense_1
,
rate
=
0.4
,
training
=
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
)
dense_2
=
tf
.
layers
.
dense
(
inputs
=
dropout_1
,
units
=
256
,
activation
=
tf
.
nn
.
relu
)
logits
=
dense_2
predictions
=

{
"classes"
:
tf
.
argmax
(
input
=
logits
,
axis
=
1
),
"probabilities"
:
tf
.
nn
.
softmax
(
logits
,
name
=
"softmax_tensor"
)
}
#
Prediction
and training
if
mode
==
tf
.
estimator
.
ModeKeys
.
PREDICT
:
return
tf
.
estimator
.
EstimatorSpec
(
mode
=
mode
,
predictions
=
predictions
)
#

Calculate

Loss

(
for
both TRAIN and EVAL modes
)
onehot_labels
=
tf
.
one_hot
(
indices
=
tf
.
cast
(
labels
,
tf
.
int32
),
depth
=
256
)
loss
=
tf
.
losses
.
softmax_cross_entropy
(
onehot_labels
=
onehot_labels
,
logits
=
logits
)
#

Compute
evaluation metrics
.
accuracy
=
tf
.
metrics
.
accuracy
(
labels
=
labels
,
predictions
=
predictions
[
"classes"
],
name
=
"acc_op"
)
metrics
=

{
"accuracy"
:
accuracy
}
tf
.
summary
.
scalar
(
"accuracy"
,
accuracy
[
1
])
#

Configure
the
Training

Op

(
for
TRAIN mode
)
if
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
:
optimizer
=
tf
.
train
.
AdagradOptimizer
(
learning_rate
=
0.001
)
train_op
=
optimizer
.
minimize
(
loss
=
loss
,
global_step
=
tf
.
train
.
get_global_step
())
return
tf
.
estimator
.
EstimatorSpec
(
mode
=
mode
,
loss
=
loss
,
train_op
=
train_op
)
#

Add
evaluation metrics
(
for
EVAL mode
)
eval_metric_ops
=

{
"accuracy"
:
tf
.
metrics
.
accuracy
(
labels
=
labels
,
predictions
=
predictions
[
"classes"
])}
return
tf
.
estimator
.
EstimatorSpec
(
mode
=
mode
,
loss
=
loss
,
eval_metric_ops
=
eval_metric_ops
)
#

Parse
CSV input file and resize image
def _parse_csv
(
line
):
parsed_line
=
tf
.
decode_csv
(
line
,

[[
""
],

[]])
filename
=
parsed_line
[
0
]
label
=
parsed_line
[
1
]
image_string
=
tf
.
read_file
(
filename
)
image_decoded
=
tf
.
image
.
decode_jpeg
(
image_string
,
channels
=
3
)
image_resized
=
tf
.
image
.
resize_images
(
image_decoded
,
image_resize
)
image_gray
=
tf
.
image
.
rgb_to_grayscale
(
image_resized
)
return
image_gray
,
label
def data_train_estimator
():
dataset
=
tf
.
data
.
TextLineDataset
(
train_csv_file
).
map
(
_parse_csv
)

#

Map
each line to convert the data
dataset
=
dataset
.
batch
(
100
)
dataset
=
dataset
.
shuffle
(
1000
)
dataset
=
dataset
.
repeat
()
iterator
=
dataset
.
make_one_shot_iterator
()

#
create one shot iterator
feature
,
label
=
iterator
.
get_next
()
return
feature
,
label
def data_test_estimator
():
dataset
=
tf
.
data
.
TextLineDataset
(
test_csv_file
).
map
(
_parse_csv
)

#

Map
each line to convert the data
dataset
=
dataset
.
batch
(
100
)
iterator
=
dataset
.
make_one_shot_iterator
()

#
create one shot iterator
feature
,
label
=
iterator
.
get_next
()
return
feature
,
label
def main
(
unused_argv
):
#
MAIN ENTRY
#

Create
the
Estimator
classifier
=
tf
.
estimator
.
Estimator
(
model_fn
=
model_fn
,
model_dir
=
"/tmp/"
+
project_name
,
params
={
#
PARAMS
}
)
classifier
.
train
(
input_fn
=
data_train_estimator
,
steps
=
30000
)
eval_results
=
classifier
.
evaluate
(
input_fn
=
data_test_estimator
)
tf
.
summary
.
scalar
(
"Accuracy"
,
eval_results
[
"accuracy"
])
print
(
eval_results
)
if
__name__
==

"__main__"
:
tf
.
app
.
run
()