class train_tf_cc_output: decoded_data_train_cc1 = np.array([]) encoded_data_train_cc1 = np.array([]) decoded_data_valid_cc1 = np.array([]) encoded_data_valid_cc1 = np.array([]) decoded_data_test_cc1 = np.array([]) encoded_data_test_cc1 = np.array([]) obj_classifier = classifier_data() def function(self): print("This is train_tensorflow_cc_output class")
class train_tf_cc_input: cc1_input_train_perm = np.array([]) cc1_output_train_perm = np.array([]) cc1_input_valid_perm = np.array([]) cc1_output_valid_perm = np.array([]) obj_classifier_data = classifier_data() dimension_hidden_layer1 = [] EPOCHS_CC = [] classI = [] classJ = [] dataset_name = [] dim_feature = [] def function(self): print("This is train_tensorflow_cc_input class")
#......................cc....................... number_of_cc = number_of_train_classes * number_of_train_classes - number_of_train_classes print "Number of c coders %d " % number_of_cc cross_coders_train_data_input = [] cross_coders_train_data_output = [] # In[ ]: #Get mean feature vector for each class mean_feature_mat = np.empty((0, dimension_visual_data), float) number_of_samples_per_class_train = [] number_of_samples_per_class_test = [] number_of_samples_per_class_valid = [] #pdb.set_trace() obj_classifier = classifier_data() cnt = 0 for classI in train_class_labels: indices = np.flatnonzero(dataset_labels == classI) classI_features = visual_features_dataset[indices.astype(int), :] mean_feature = classI_features.mean(0) mean_feature_mat = np.append(mean_feature_mat, mean_feature.reshape(1, dimension_visual_data), axis=0) number_of_samples_per_class_train.append( int(TR_TS_VA_SPLIT[0] * np.size(indices))) number_of_samples_per_class_test.append( int(TR_TS_VA_SPLIT[2] * np.size(indices))) number_of_samples_per_class_valid.append( int(TR_TS_VA_SPLIT[1] * np.size(indices)))