my_Mnist_Model / config.json
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{
"model_type": "cnn",
"architecture": {
"type": "sequential",
"layers": [
{
"type": "data_augmentation"
},
{
"type": "conv2d",
"filters": 32,
"kernel_size": 3,
"activation": "relu"
},
{
"type": "batch_normalization"
},
{
"type": "max_pooling2d",
"pool_size": 2
},
{
"type": "conv2d",
"filters": 64,
"kernel_size": 3,
"activation": "relu"
},
{
"type": "batch_normalization"
},
{
"type": "max_pooling2d",
"pool_size": 2
},
{
"type": "flatten"
},
{
"type": "dense",
"units": 256,
"activation": "relu"
},
{
"type": "batch_normalization"
},
{
"type": "dropout",
"rate": 0.3
},
{
"type": "dense",
"units": 128,
"activation": "relu"
},
{
"type": "batch_normalization"
},
{
"type": "dropout",
"rate": 0.2
},
{
"type": "dense",
"units": 10,
"activation": "softmax"
}
]
},
"training_config": {
"optimizer": {
"type": "adam",
"learning_rate": 0.001,
"beta_1": 0.9,
"beta_2": 0.999
},
"loss": "sparse_categorical_crossentropy",
"metrics": [
"accuracy"
],
"epochs": 20,
"batch_size": 32,
"validation_split": 0.2
},
"preprocessing": {
"input_normalization": "divide_by_255",
"input_shape": [
28,
28,
1
]
}
}