transformer_fun=None) am_test_batch = batch_data.batch_data(amte, normalizer_fun=None,transformer_fun=None) # Spit out some sample data next_batch = am_train_batch.next() data, label = next_batch np.set_printoptions(threshold=np.nan) print "Batch properties:" print "Length: {}".format(len(data)) print "Type: {}".format(type(data)) print print "First record of first batch:" print "Type (1 level in): {}".format(type(data[0])) print "Type of record (2 levels in): {}".format(type(data[0,0])) print data[0,0] print "Sentiment label: {}".format(label[0]) print "In numpy format:" oh = data_utils.to_one_hot(data[0,0]) print np.array_str(np.argmax(oh,axis=0)) print "Translated back into characters:\n" print data_utils.from_one_hot(oh) # dimension checks second_batch_data, second_batch_label = second_batch = am_train_batch.next() second_batch = list(second_batch) print len(second_batch) print "Data object type: ", type(second_batch_data) print second_batch_data.shape
# Spit out some sample data next_batch = amz_train_batch.next() data, label = next_batch np.set_printoptions(threshold=np.nan) print "Batch properties:" print "Shape (data): {}".format(data.shape) print "Shape (label): {}".format(label.shape) print "Type: {}".format(type(data)) print print "First record of first batch:" print "Type (1 level in): {}".format(type(data[0])) print "Type of record (2 levels in): {}".format(type(data[0,0])) print data[0,0] print "Sentiment label: {}".format(label[0,0]) print "Data in numpy format:" oh = data_utils.to_one_hot(data[0,0]) print np.array_str(np.argmax(oh,axis=0)) print "Translated back into characters:\n" print ''.join(data_utils.from_one_hot(oh)) # demo balanced batching amz_balanced_batcher = batch_data(amz_train,balance_labels=True) balanced_batch = amz_balanced_batcher.next() print 'Balanced batch:' balanced_label_counts = {} for idx in range(balanced_batch[1].shape[0]): label = balanced_batch[1][idx,0] balanced_label_counts[label] = balanced_label_counts.get(label, 0) + 1 print balanced_label_counts # Demo iterator utility classes
print(f"acc: {acc}") if __name__ == '__main__': print("Testing network on scikit-learn wine dataset...") np.random.seed(1) scaler = StandardScaler() print("\nLoading data...") data = load_wine() print("\nPreprocessing data...") x = data['data'] y = data['target'] y = data_utils.to_one_hot(y) x_train, y_train, x_test, y_test, x_valid, y_valid = data_utils.train_test_valid_split( x, y, 0.7, 0.2, should_shuffle=True) x_train = scaler.fit_transform(x_train) x_test = scaler.transform(x_test) x_valid = scaler.transform(x_valid) dataset = data_utils.Dataset(x_train, y_train) data_manager = data_utils.DataManager(dataset, batch_size=8) print("\nBuilding network...") n_inputs = len(x[0]) n_hidden = 2 n_outputs = 3