Verified the code identification.
Run
GenerateImg.py, change num to set train set num(90%) and test set num(10%).
In
IdentifyImg.py, some basic data set, deNoising(filter), generate data, preprocessing and generate train model.
correct_rate now is reaching 93%
# model
layers.Conv2D(32, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Dropout(0.5),
layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Dropout(0.3),
layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(2480),
layers.Dense(248), # 4*62
layers.Reshape([4, 62]).
├── README.md
├── __pycache__
├── logs
├── test
│ └── xxx.png
├── train
│ └── xxx.png
├── GenerateImg.py
├── IdentifyImg.py
├── IdentifyTest.py
├── IdentifyTrain.py
└── model.h5
4 directories, 6 files
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