def plot_compare_spec_results2(model, proc): import cPickle from smartNN.utils.utils import tile_raster_graphs from PIL.Image import fromarray import smartNN.datasets.preprocessor as processor with open(os.environ['smartNN_SAVE_PATH'] + '/log/' + model + '/model.pkl', 'rb') as f: mlp = cPickle.load(f) start = -12 end = -10 tile_shape=(2,1) print('..loading data from spec.double') folder_path = os.environ['smartNN_DATA_PATH'] + '/p276_double' dct_data = extract_examples(folder_path, -10, -1) dct_data = dct_data[start:end] print('..loading data from ' + model) data = P276(train_valid_test_ratio=[8,1,1]) test = data.get_test() # prep = Standardize() # prep = Scale() prep = getattr(processor, proc)() if prep.__class__.__name__ == 'Scale': prep.max = 98803.031 prep.min = 0.1 print('..preprocessing data ' + prep.__class__.__name__ ) proc_test_X = prep.apply(test.X) dct_data = prep.apply(dct_data) print('..fprop X') output = mlp.fprop(proc_test_X) print('..saving data') plt = tile_raster_graphs(dct_data, proc_test_X[start:end], output[start:end], slice=(0,-1), tile_shape=tile_shape, tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_2049_all2.png') plt.close() plt = tile_raster_graphs(dct_data, proc_test_X[-12:-10], output[-12:-10], slice=(0,200), tile_shape=(2,1), tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_200_all2.png') plt.close() plt = tile_raster_graphs(dct_data, proc_test_X[-12:-10], output[-12:-10], slice=(1500,-1), tile_shape=(2,1), tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_1500_2400_all2.png') plt.close() print('Saved Successfully')
def plot_compare_spec_results(model, proc): import cPickle from smartNN.utils.utils import tile_raster_graphs from PIL.Image import fromarray import smartNN.datasets.preprocessor as processor with open(os.environ['smartNN_SAVE_PATH'] + '/log/' + model + '/model.pkl', 'rb') as f: mlp = cPickle.load(f) # data = Mnist(preprocessor = None, # binarize = False, # batch_size = 100, # num_batches = None, # train_ratio = 5, # valid_ratio = 1, # iter_class = 'SequentialSubsetIterator', # rng = None) start = -12 end = -10 tile_shape=(2,1) print('..loading data from ' + model) data = P276(train_valid_test_ratio=[8,1,1]) test = data.get_test() # prep = Standardize() # prep = Scale() prep = getattr(processor, proc)() if prep.__class__.__name__ == 'Scale': prep.max = 98803.031 prep.min = 0.1 print('..preprocessing data ' + prep.__class__.__name__ ) proc_test_X = prep.apply(test.X) print('..fprop X') output = mlp.fprop(proc_test_X) print('..saving data') plt = tile_raster_graphs(proc_test_X[start:end], output[start:end], slice=(0,-1), tile_shape=tile_shape, tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_2049.png') plt.close() plt = tile_raster_graphs(proc_test_X[-12:-10], output[-12:-10], slice=(0,200), tile_shape=(2,1), tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_200.png') plt.close() plt = tile_raster_graphs(proc_test_X[-12:-10], output[-12:-10], slice=(1500,-1), tile_shape=(2,1), tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_1500_2400.png') plt.close() print('Saved Successfully')
def plot_spec(spec): with open(spec) as f: spec_data = np.fromfile(f, dtype='<f4', count=-1) plt = tile_raster_graphs(spec_data, spec_data, slice=(0,-1), tile_shape=(2,1), tile_spacing=(0.1,0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/spec.png') plt.show() plt.close()
def plot_spec(spec): with open(spec) as f: spec_data = np.fromfile(f, dtype='<f4', count=-1) plt = tile_raster_graphs(spec_data, spec_data, slice=(0, -1), tile_shape=(2, 1), tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/spec.png') plt.show() plt.close()
def plot_compare_spec_results(model, proc): import cPickle from smartNN.utils.utils import tile_raster_graphs from PIL.Image import fromarray import smartNN.datasets.preprocessor as processor with open(os.environ['smartNN_SAVE_PATH'] + '/log/' + model + '/model.pkl', 'rb') as f: mlp = cPickle.load(f) # data = Mnist(preprocessor = None, # binarize = False, # batch_size = 100, # num_batches = None, # train_ratio = 5, # valid_ratio = 1, # iter_class = 'SequentialSubsetIterator', # rng = None) start = -12 end = -10 tile_shape = (2, 1) print('..loading data from ' + model) data = P276(train_valid_test_ratio=[8, 1, 1]) test = data.get_test() # prep = Standardize() # prep = Scale() prep = getattr(processor, proc)() if prep.__class__.__name__ == 'Scale': prep.max = 98803.031 prep.min = 0.1 print('..preprocessing data ' + prep.__class__.__name__) proc_test_X = prep.apply(test.X) print('..fprop X') output = mlp.fprop(proc_test_X) print('..saving data') plt = tile_raster_graphs(proc_test_X[start:end], output[start:end], slice=(0, -1), tile_shape=tile_shape, tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_2049.png') plt.close() plt = tile_raster_graphs(proc_test_X[-12:-10], output[-12:-10], slice=(0, 200), tile_shape=(2, 1), tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_200.png') plt.close() plt = tile_raster_graphs(proc_test_X[-12:-10], output[-12:-10], slice=(1500, -1), tile_shape=(2, 1), tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_1500_2400.png') plt.close() print('Saved Successfully')
def plot_compare_spec_results2(model, proc): import cPickle from smartNN.utils.utils import tile_raster_graphs from PIL.Image import fromarray import smartNN.datasets.preprocessor as processor with open(os.environ['smartNN_SAVE_PATH'] + '/log/' + model + '/model.pkl', 'rb') as f: mlp = cPickle.load(f) start = -12 end = -10 tile_shape = (2, 1) print('..loading data from spec.double') folder_path = os.environ['smartNN_DATA_PATH'] + '/p276_double' dct_data = extract_examples(folder_path, -10, -1) dct_data = dct_data[start:end] print('..loading data from ' + model) data = P276(train_valid_test_ratio=[8, 1, 1]) test = data.get_test() # prep = Standardize() # prep = Scale() prep = getattr(processor, proc)() if prep.__class__.__name__ == 'Scale': prep.max = 98803.031 prep.min = 0.1 print('..preprocessing data ' + prep.__class__.__name__) proc_test_X = prep.apply(test.X) dct_data = prep.apply(dct_data) print('..fprop X') output = mlp.fprop(proc_test_X) print('..saving data') plt = tile_raster_graphs(dct_data, proc_test_X[start:end], output[start:end], slice=(0, -1), tile_shape=tile_shape, tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_2049_all2.png') plt.close() plt = tile_raster_graphs(dct_data, proc_test_X[-12:-10], output[-12:-10], slice=(0, 200), tile_shape=(2, 1), tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_0_200_all2.png') plt.close() plt = tile_raster_graphs(dct_data, proc_test_X[-12:-10], output[-12:-10], slice=(1500, -1), tile_shape=(2, 1), tile_spacing=(0.1, 0.1), legend=True) plt.savefig(os.environ['smartNN_SAVE_PATH'] + '/images/' + model + '_1500_2400_all2.png') plt.close() print('Saved Successfully')