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