patch_size = (8, 8)
cs_args = {
"train_args":{
"L1_reg": 1e-06,
"learning_rate": 0.05,
"L2_reg": 1e-05,
"nepochs": 1,
"cost_type": "crossentropy",
"save_exp_data": False,
"batch_size": 200,
"normalize_weights": False
},
"test_args":{
"save_exp_data":False,
"batch_size": 200
}
}
no_of_patches = 64
print "starting pretrain"
prmlp = PatchBasedMLP(pre_input,
n_in=patch_size[0] * patch_size[1],
n_hiddens=[2048, 2048],
n_out=11,
patch_size=patch_size)
incremental_data_experiment(prmlp, train_datasets, test_datasets, no_of_patches=no_of_patches,
patch_size=patch_size, **cs_args)
}
}
print "Starting the cross-validation"
x = T.matrix('x')
n_hiddens = [2048]
train_set_patches, train_set_pre, train_set_labels = ds.Xtrain_patches, ds.Xtrain_presences, ds.Ytrain
test_set_patches, test_set_pre, test_set_labels = ds.Xtest_patches, ds.Xtest_presences, ds.Ytest
prmlp = PatchBasedMLP(x,
n_in=patch_size[0] * patch_size[1],
n_hiddens=n_hiddens,
n_out=11,
no_of_patches=no_of_patches,
activation=NeuralActivations.Rectifier,
use_adagrad=False,
quiet=True)
costs, pretrain_probs = prmlp.train(train_set_patches, train_set_pre,
**cs_args["train_args"])
save_probs((pretrain_probs, train_set_labels), train_file)
print "Testing on the training dataset."
fin_test_score, post_test_train_probs = prmlp.test(train_set_patches,
train_set_pre,
**cs_args["test_args"])
save_probs((post_test_train_probs, train_set_labels), train_test_file)
"batch_size": 200
}
}
print "Starting the cross-validation"
x = T.matrix('x')
n_hiddens = [2048]
train_set_patches, train_set_pre = ds.Xtrain_patches, ds.Xtrain_presences
test_set_patches, test_set_pre = ds.Xtest_patches, ds.Xtest_presences
prmlp = PatchBasedMLP(x,
n_in=patch_size[0] * patch_size[1],
n_hiddens=n_hiddens,
n_out=11,
no_of_patches=no_of_patches,
activation=NeuralActivations.Rectifier,
use_adagrad=False,
quiet=True)
prmlp.train(train_set_patches, train_set_pre, **cs_args["train_args"])
prmlp.save_data()
print "Testing on the training dataset."
prmlp.test(train_set_patches, train_set_pre, **cs_args["test_args"])
print "Testing on the test dataset."
prmlp.test(test_set_patches, test_set_pre, **cs_args["test_args"])
"save_exp_data": False,
"batch_size": 200
}
}
print "Starting the cross-validation"
x = T.matrix('x')
n_hiddens = [2048]
train_set_patches, train_set_pre, train_set_labels = ds.Xtrain_patches, ds.Xtrain_presences, ds.Ytrain
test_set_patches, test_set_pre, test_set_labels = ds.Xtest_patches, ds.Xtest_presences, ds.Ytest
prmlp = PatchBasedMLP(x, n_in=patch_size[0] * patch_size[1],
n_hiddens=n_hiddens, n_out=11, no_of_patches=no_of_patches,
activation=NeuralActivations.Rectifier, use_adagrad=False, quiet=True)
costs, pretrain_probs = prmlp.train(train_set_patches, train_set_pre, **cs_args["train_args"])
save_probs((pretrain_probs, train_set_labels), train_file)
print "Testing on the training dataset."
fin_test_score, post_test_train_probs = prmlp.test(train_set_patches, train_set_pre, **cs_args["test_args"])
save_probs((post_test_train_probs, train_set_labels), train_test_file)
print "Testing on the test dataset."
fin_test_score, post_test_probs = prmlp.test(test_set_patches, test_set_pre, **cs_args["test_args"])
save_probs((post_test_probs, test_set_labels), test_file)
data_path_40k = "/RQusagers/gulcehre/dataset/pentomino/pieces/pento64x64_40k_seed_39112222.npy"
data_path = "/RQusagers/gulcehre/dataset/pentomino/experiment_data/pento64x64_80k_seed_39112222.npy"
patch_size = (8, 8)
ds.setup_pretraining_dataset(data_path=data_path_40k,
patch_size=patch_size,
normalize_inputs=False)
x = T.matrix('x')
n_hiddens = [1024, 768]
no_of_patches = 64
no_of_classes = 11
prmlp = PatchBasedMLP(x,
n_in=patch_size[0] * patch_size[1],
n_hiddens=n_hiddens,
n_out=11,
no_of_patches=no_of_patches,
activation=NeuralActivations.Rectifier,
use_adagrad=False)
params = [prmlp.params[0], prmlp.params[2], prmlp.params[4]]
post_mlp = PostMLP(x,
n_in=no_of_patches * no_of_classes,
n_hiddens=n_hiddens,
n_out=1,
use_adagrad=False)
pre_training(patch_mlp=prmlp, post_mlp=post_mlp, ds=ds)
"batch_size": 200,
"normalize_weights": False
},
"test_args": {
"save_exp_data": False,
"batch_size": 200
}
}
print "Starting the cross-validation"
x = T.matrix('x')
n_hiddens = [2048]
train_set_patches, train_set_pre = ds.Xtrain_patches, ds.Xtrain_presences
test_set_patches, test_set_pre = ds.Xtest_patches, ds.Xtest_presences
prmlp = PatchBasedMLP(x, n_in=patch_size[0] * patch_size[1],
n_hiddens=n_hiddens, n_out=11, no_of_patches=no_of_patches,
activation=NeuralActivations.Rectifier, use_adagrad=False, quiet=True)
prmlp.train(train_set_patches, train_set_pre, **cs_args["train_args"])
prmlp.save_data()
print "Testing on the training dataset."
prmlp.test(train_set_patches, train_set_pre, **cs_args["test_args"])
print "Testing on the test dataset."
prmlp.test(test_set_patches, test_set_pre, **cs_args["test_args"])
if __name__ == "__main__":
print "Task has just started."
print "Loading the dataset"
ds = Dataset()
patch_size = (8, 8)
ds_path = \
"/RQusagers/gulcehre/dataset/pentomino/experiment_data/pento64x64_80k_seed_39112222.npy"
data_new =\
"/RQusagers/gulcehre/dataset/pentomino/rnd_pieces/pento64x64_5k_seed_43112222_64patches_rnd.npy"
data_new_40k =\
"/RQexec/gulcehre/datasets/pentomino/pento_64x64_8x8patches/pento64x64_40k_64patches_seed_975168712_64patches.npy"
ds.setup_pretraining_dataset(data_path=data_new_40k,
patch_size=patch_size,
normalize_inputs=False)
pre_input = T.matrix('pre_input')
n_hiddens = [2048]
prmlp = PatchBasedMLP(pre_input,
n_in=8 * 8,
n_hiddens=n_hiddens,
n_out=11,
no_of_patches=64,
activation=NeuralActivations.Rectifier,
use_adagrad=False)
csvm = CSVM()
pre_training(prmlp, csvm, ds)
