def create_experiment_json_fn(output_dir):
X_train, Y_train, X_test, Y_test = load_mnist()
config = {
'name': 'real_mnsit',
'output_dir': output_dir,
'eval_every_n_steps': 5,
'run_config': {'save_checkpoints_steps': 100},
'train_input_data_config': {
'input_type': plx.configs.InputDataConfig.NUMPY,
'pipeline_config': {'name': 'train', 'batch_size': 64, 'num_epochs': 5,
'shuffle': True},
'x': X_train,
'y': Y_train
},
'eval_input_data_config': {
'input_type': plx.configs.InputDataConfig.NUMPY,
'pipeline_config': {'name': 'eval', 'batch_size': 32, 'num_epochs': 1,
'shuffle': False},
'x': X_test,
'y': Y_test
},
'estimator_config': {'output_dir': output_dir},
'model_config': {
'model_type': 'classifier',
'loss_config': {'name': 'sigmoid_cross_entropy'},
'eval_metrics_config': [{'name': 'streaming_accuracy'}],
'optimizer_config': {'name': 'Adam', 'learning_rate': 0.01},
'graph_config': {
'name': 'mnist',
'definition': [
(plx.layers.Conv2d,
{'num_filter': 32, 'filter_size': 3, 'strides': 1, 'activation': 'elu',
'regularizer': 'l2_regularizer'}),
(plx.layers.MaxPool2d, {'kernel_size': 2}),
(plx.layers.LocalResponseNormalization, {}),
(plx.layers.Conv2d, {'num_filter': 64, 'filter_size': 3, 'activation': 'relu',
'regularizer': 'l2_regularizer'}),
(plx.layers.MaxPool2d, {'kernel_size': 2}),
(plx.layers.LocalResponseNormalization, {}),
(plx.layers.FullyConnected, {'n_units': 128, 'activation': 'tanh'}),
(plx.layers.Dropout, {'keep_prob': 0.8}),
(plx.layers.FullyConnected, {'n_units': 256, 'activation': 'tanh'}),
(plx.layers.Dropout, {'keep_prob': 0.8}),
(plx.layers.FullyConnected, {'n_units': 10}),
]
}
}
}
experiment_config = plx.configs.ExperimentConfig.read_configs(config)
return plx.experiments.create_experiment(experiment_config)
def create_experiment_json_fn(output_dir):
X_train, Y_train, X_test, Y_test = load_mnist()
config = {
'name': 'lenet_mnsit',
'output_dir': output_dir,
'eval_every_n_steps': 100,
'train_steps_per_iteration': 100,
'run_config': {
'save_checkpoints_steps': 100
},
'train_input_data_config': {
'input_type': plx.configs.InputDataConfig.NUMPY,
'pipeline_config': {
'name': 'train',
'batch_size': 64,
'num_epochs': None,
'shuffle': True
},
'x': X_train,
'y': Y_train
},
'eval_input_data_config': {
'input_type': plx.configs.InputDataConfig.NUMPY,
'pipeline_config': {
'name': 'eval',
'batch_size': 32,
'num_epochs': None,
'shuffle': False
},
'x': X_test,
'y': Y_test
},
'estimator_config': {
'output_dir': output_dir
},
'model_config': {
'summaries':
'all',
'model_type':
'classifier',
'loss_config': {
'name': 'softmax_cross_entropy'
},
'eval_metrics_config': [{
'name': 'streaming_accuracy'
}, {
'name': 'streaming_precision'
}],
'optimizer_config': {
'name': 'Adam',
'learning_rate': 0.002,
'decay_type': 'exponential_decay',
'decay_rate': 0.2
},
'graph_config': {
'name':
'lenet',
'definition': [
(plx.layers.Conv2d, {
'num_filter': 32,
'filter_size': 5,
'strides': 1,
'regularizer': 'l2_regularizer'
}),
(plx.layers.MaxPool2d, {
'kernel_size': 2
}),
(plx.layers.Conv2d, {
'num_filter': 64,
'filter_size': 5,
'regularizer': 'l2_regularizer'
}),
(plx.layers.MaxPool2d, {
'kernel_size': 2
}),
(plx.layers.FullyConnected, {
'n_units': 1024,
'activation': 'tanh'
}),
(plx.layers.FullyConnected, {
'n_units': 10
}),
]
}
}
}
experiment_config = plx.configs.ExperimentConfig.read_configs(config)
return plx.experiments.create_experiment(experiment_config)