x = BatchNormalization()(x)
x = Flatten()(x)
x = Dense(1200, activation='relu')(x)
x = BatchNormalization()(x)
return Model(inputs=_input, outputs=x, name='embedding')
all_features = read.read()
test_ids = list(all_features.keys())
all_labels = list(all_features[test_ids[0]].keys())
for test_id in test_ids:
for a_label in all_labels:
train_labels = [a for a in all_labels if a != a_label]
_train_features, _test_features = split(all_features, test_id)
_train_features = read.remove_class(_train_features, [a_label])
_support_features, _test_features = read.support_set_split(
_test_features, k_shot)
_train_features, _train_labels = flatten(_train_features)
_support_features, _support_labels = flatten(_support_features)
id_list = range(len(train_labels))
activity_id_dict = dict(zip(train_labels, id_list))
_train_labels_ = []
for item in _train_labels:
_train_labels_.append(activity_id_dict.get(item))
_train_labels_ = np_utils.to_categorical(_train_labels_,
base_input = Input((input_shape, ))
x = Dense(1200, activation='relu')(base_input)
embedding_model = Model(base_input, x, name='embedding')
return embedding_model
feature_data = read.read()
test_ids = list(feature_data.keys())
all_labels = list(feature_data[test_ids[0]].keys())
for test_id in test_ids:
for a_label in all_labels:
train_labels = [a for a in all_labels if a != a_label]
_train_data, _test_data = read.split(feature_data, test_id)
_train_data = read.remove_class(_train_data, [a_label])
_support_data, _test_data = read.support_set_split(_test_data, k_shot)
_train_data, _train_labels = read.flatten(_train_data)
_support_data, _support_labels = read.flatten(_support_data)
_train_data = np.array(_train_data)
_support_data = np.array(_support_data)
_train_labels = np.array(_train_labels)
_support_labels = np.array(_support_labels)
base_network = build_mlp_model(feature_length)
input_a = Input(shape=(feature_length, ))
for test_id in test_ids:
for _int in range(5):
test_labels_indices = np.random.choice(len(all_labels),
num_test_classes, False)
test_labels = [
a for ii, a in enumerate(all_labels) if ii in test_labels_indices
]
print(test_labels)
train_labels = [
a for ii, a in enumerate(all_labels)
if ii not in test_labels_indices
]
print(train_labels)
_train_data, _test_data = read.split(feature_data, test_id)
_train_data = read.remove_class(_train_data, test_labels)
_support_data, _test_data = read.support_set_split(_test_data, k_shot)
_train_data, _train_labels = read.flatten(_train_data)
_support_data, _support_labels = read.flatten(_support_data)
_train_data = np.array(_train_data)
_train_data = np.expand_dims(_train_data, 3)
_support_data = np.array(_support_data)
_support_data = np.expand_dims(_support_data, 3)
_train_labels = np.array(_train_labels)
_support_labels = np.array(_support_labels)
base_network = build_conv_model()
