python - InvalidArgumentError：记录中的字段 N 不是有效的 int32，TF.make_csv_dataset 中的错误

Question

我有两个 csv 文件。一个包含大约 700 列的列在 Tensorflow 输入管道中正确加载，但第二个包含 700 个相同列和大约 100 个额外列的第二个未正确加载。以下是存储在Google Drive上的文件。这是我的代码

import tensorflow as tf
all_columns=['frame', 'face_id', 'timestamp', 'confidence', 'success','gaze_0_x', 'gaze_0_y', 'gaze_0_z', 'gaze_1_x', 'gaze_1_y', 'gaze_1_z', 'gaze_angle_x', 'gaze_angle_y', 'eye_lmk_x_0', 'eye_lmk_x_1', 'eye_lmk_x_2', 'eye_lmk_x_3', 'eye_lmk_x_4', 'eye_lmk_x_5', 'eye_lmk_x_6', 'eye_lmk_x_7', 'eye_lmk_x_8', 'eye_lmk_x_9', 'eye_lmk_x_10', 'eye_lmk_x_11', 'eye_lmk_x_12', 'eye_lmk_x_13', 'eye_lmk_x_14', 'eye_lmk_x_15', 'eye_lmk_x_16', 'eye_lmk_x_17', 'eye_lmk_x_18', 'eye_lmk_x_19', 'eye_lmk_x_20', 'eye_lmk_x_21', 'eye_lmk_x_22', 'eye_lmk_x_23', 'eye_lmk_x_24', 'eye_lmk_x_25', 'eye_lmk_x_26', 'eye_lmk_x_27', 'eye_lmk_x_28', 'eye_lmk_x_29', 'eye_lmk_x_30', 'eye_lmk_x_31', 'eye_lmk_x_32', 'eye_lmk_x_33', 'eye_lmk_x_34', 'eye_lmk_x_35', 'eye_lmk_x_36', 'eye_lmk_x_37', 'eye_lmk_x_38', 'eye_lmk_x_39', 'eye_lmk_x_40', 'eye_lmk_x_41', 'eye_lmk_x_42', 'eye_lmk_x_43', 'eye_lmk_x_44', 'eye_lmk_x_45', 'eye_lmk_x_46', 'eye_lmk_x_47', 'eye_lmk_x_48', 'eye_lmk_x_49', 'eye_lmk_x_50', 'eye_lmk_x_51', 'eye_lmk_x_52', 'eye_lmk_x_53', 'eye_lmk_x_54', 'eye_lmk_x_55', 'eye_lmk_y_0', 'eye_lmk_y_1', 'eye_lmk_y_2', 'eye_lmk_y_3', 'eye_lmk_y_4', 'eye_lmk_y_5', 'eye_lmk_y_6', 'eye_lmk_y_7', 'eye_lmk_y_8', 'eye_lmk_y_9', 'eye_lmk_y_10', 'eye_lmk_y_11', 'eye_lmk_y_12', 'eye_lmk_y_13', 'eye_lmk_y_14', 'eye_lmk_y_15', 'eye_lmk_y_16', 'eye_lmk_y_17', 'eye_lmk_y_18', 'eye_lmk_y_19', 'eye_lmk_y_20', 'eye_lmk_y_21', 'eye_lmk_y_22', 'eye_lmk_y_23', 'eye_lmk_y_24', 'eye_lmk_y_25', 'eye_lmk_y_26', 'eye_lmk_y_27', 'eye_lmk_y_28', 'eye_lmk_y_29', 'eye_lmk_y_30', 'eye_lmk_y_31', 'eye_lmk_y_32', 'eye_lmk_y_33', 'eye_lmk_y_34', 'eye_lmk_y_35', 'eye_lmk_y_36', 'eye_lmk_y_37', 'eye_lmk_y_38', 'eye_lmk_y_39', 'eye_lmk_y_40', 'eye_lmk_y_41', 'eye_lmk_y_42', 'eye_lmk_y_43', 'eye_lmk_y_44', 'eye_lmk_y_45', 'eye_lmk_y_46', 'eye_lmk_y_47', 'eye_lmk_y_48', 'eye_lmk_y_49', 'eye_lmk_y_50', 'eye_lmk_y_51', 'eye_lmk_y_52', 'eye_lmk_y_53', 'eye_lmk_y_54', 'eye_lmk_y_55', 'eye_lmk_X_0', 'eye_lmk_X_1', 'eye_lmk_X_2', 'eye_lmk_X_3', 'eye_lmk_X_4', 'eye_lmk_X_5', 'eye_lmk_X_6', 'eye_lmk_X_7', 'eye_lmk_X_8', 'eye_lmk_X_9', 'eye_lmk_X_10', 'eye_lmk_X_11', 'eye_lmk_X_12', 'eye_lmk_X_13', 'eye_lmk_X_14', 'eye_lmk_X_15', 'eye_lmk_X_16', 'eye_lmk_X_17', 'eye_lmk_X_18', 'eye_lmk_X_19', 'eye_lmk_X_20', 'eye_lmk_X_21', 'eye_lmk_X_22', 'eye_lmk_X_23', 'eye_lmk_X_24', 'eye_lmk_X_25', 'eye_lmk_X_26', 'eye_lmk_X_27', 'eye_lmk_X_28', 'eye_lmk_X_29', 'eye_lmk_X_30', 'eye_lmk_X_31', 'eye_lmk_X_32', 'eye_lmk_X_33', 'eye_lmk_X_34', 'eye_lmk_X_35', 'eye_lmk_X_36', 'eye_lmk_X_37', 'eye_lmk_X_38', 'eye_lmk_X_39', 'eye_lmk_X_40', 'eye_lmk_X_41', 'eye_lmk_X_42', 'eye_lmk_X_43', 'eye_lmk_X_44', 'eye_lmk_X_45', 'eye_lmk_X_46', 'eye_lmk_X_47', 'eye_lmk_X_48', 'eye_lmk_X_49', 'eye_lmk_X_50', 'eye_lmk_X_51', 'eye_lmk_X_52', 'eye_lmk_X_53', 'eye_lmk_X_54', 'eye_lmk_X_55', 'eye_lmk_Y_0', 'eye_lmk_Y_1', 'eye_lmk_Y_2', 'eye_lmk_Y_3', 'eye_lmk_Y_4', 'eye_lmk_Y_5', 'eye_lmk_Y_6', 'eye_lmk_Y_7', 'eye_lmk_Y_8', 'eye_lmk_Y_9', 'eye_lmk_Y_10', 'eye_lmk_Y_11', 'eye_lmk_Y_12', 'eye_lmk_Y_13', 'eye_lmk_Y_14', 'eye_lmk_Y_15', 'eye_lmk_Y_16', 'eye_lmk_Y_17', 'eye_lmk_Y_18', 'eye_lmk_Y_19', 'eye_lmk_Y_20', 'eye_lmk_Y_21', 'eye_lmk_Y_22', 'eye_lmk_Y_23', 'eye_lmk_Y_24', 'eye_lmk_Y_25', 'eye_lmk_Y_26', 'eye_lmk_Y_27', 'eye_lmk_Y_28', 'eye_lmk_Y_29', 'eye_lmk_Y_30', 'eye_lmk_Y_31', 'eye_lmk_Y_32', 'eye_lmk_Y_33', 'eye_lmk_Y_34', 'eye_lmk_Y_35', 'eye_lmk_Y_36', 'eye_lmk_Y_37', 'eye_lmk_Y_38', 'eye_lmk_Y_39', 'eye_lmk_Y_40', 'eye_lmk_Y_41', 'eye_lmk_Y_42', 'eye_lmk_Y_43', 'eye_lmk_Y_44', 'eye_lmk_Y_45', 'eye_lmk_Y_46', 'eye_lmk_Y_47', 'eye_lmk_Y_48', 'eye_lmk_Y_49', 'eye_lmk_Y_50', 'eye_lmk_Y_51', 'eye_lmk_Y_52', 'eye_lmk_Y_53', 'eye_lmk_Y_54', 'eye_lmk_Y_55', 'eye_lmk_Z_0', 'eye_lmk_Z_1', 'eye_lmk_Z_2', 'eye_lmk_Z_3', 'eye_lmk_Z_4', 'eye_lmk_Z_5', 'eye_lmk_Z_6', 'eye_lmk_Z_7', 'eye_lmk_Z_8', 'eye_lmk_Z_9', 'eye_lmk_Z_10', 'eye_lmk_Z_11', 'eye_lmk_Z_12', 'eye_lmk_Z_13', 'eye_lmk_Z_14', 'eye_lmk_Z_15', 'eye_lmk_Z_16', 'eye_lmk_Z_17', 'eye_lmk_Z_18', 'eye_lmk_Z_19', 'eye_lmk_Z_20', 'eye_lmk_Z_21', 'eye_lmk_Z_22', 'eye_lmk_Z_23', 'eye_lmk_Z_24', 'eye_lmk_Z_25', 'eye_lmk_Z_26', 'eye_lmk_Z_27', 'eye_lmk_Z_28', 'eye_lmk_Z_29', 'eye_lmk_Z_30', 'eye_lmk_Z_31', 'eye_lmk_Z_32', 'eye_lmk_Z_33', 'eye_lmk_Z_34', 'eye_lmk_Z_35', 'eye_lmk_Z_36', 'eye_lmk_Z_37', 'eye_lmk_Z_38', 'eye_lmk_Z_39', 'eye_lmk_Z_40', 'eye_lmk_Z_41', 'eye_lmk_Z_42', 'eye_lmk_Z_43', 'eye_lmk_Z_44', 'eye_lmk_Z_45', 'eye_lmk_Z_46', 'eye_lmk_Z_47', 'eye_lmk_Z_48', 'eye_lmk_Z_49', 'eye_lmk_Z_50', 'eye_lmk_Z_51', 'eye_lmk_Z_52', 'eye_lmk_Z_53', 'eye_lmk_Z_54', 'eye_lmk_Z_55', 'pose_Tx', 'pose_Ty', 'pose_Tz', 'pose_Rx', 'pose_Ry', 'pose_Rz', 'x_0', 'x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'x_6', 'x_7', 'x_8', 'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'x_16', 'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22', 'x_23', 'x_24', 'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30', 'x_31', 'x_32', 'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38', 'x_39', 'x_40', 'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46', 'x_47', 'x_48', 'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54', 'x_55', 'x_56', 'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62', 'x_63', 'x_64', 'x_65', 'x_66', 'x_67', 'y_0', 'y_1', 'y_2', 'y_3', 'y_4', 'y_5', 'y_6', 'y_7', 'y_8', 'y_9', 'y_10', 'y_11', 'y_12', 'y_13', 'y_14', 'y_15', 'y_16', 'y_17', 'y_18', 'y_19', 'y_20', 'y_21', 'y_22', 'y_23', 'y_24', 'y_25', 'y_26', 'y_27', 'y_28', 'y_29', 'y_30', 'y_31', 'y_32', 'y_33', 'y_34', 'y_35', 'y_36', 'y_37', 'y_38', 'y_39', 'y_40', 'y_41', 'y_42', 'y_43', 'y_44', 'y_45', 'y_46', 'y_47', 'y_48', 'y_49', 'y_50', 'y_51', 'y_52', 'y_53', 'y_54', 'y_55', 'y_56', 'y_57', 'y_58', 'y_59', 'y_60', 'y_61', 'y_62', 'y_63', 'y_64', 'y_65', 'y_66', 'y_67', 'X_0', 'X_1', 'X_2', 'X_3', 'X_4', 'X_5', 'X_6', 'X_7', 'X_8', 'X_9', 'X_10', 'X_11', 'X_12', 'X_13', 'X_14', 'X_15', 'X_16', 'X_17', 'X_18', 'X_19', 'X_20', 'X_21', 'X_22', 'X_23', 'X_24', 'X_25', 'X_26', 'X_27', 'X_28', 'X_29', 'X_30', 'X_31', 'X_32', 'X_33', 'X_34', 'X_35', 'X_36', 'X_37', 'X_38', 'X_39', 'X_40', 'X_41', 'X_42', 'X_43', 'X_44', 'X_45', 'X_46', 'X_47', 'X_48', 'X_49', 'X_50', 'X_51', 'X_52', 'X_53', 'X_54', 'X_55', 'X_56', 'X_57', 'X_58', 'X_59', 'X_60', 'X_61', 'X_62', 'X_63', 'X_64', 'X_65', 'X_66', 'X_67', 'Y_0', 'Y_1', 'Y_2', 'Y_3', 'Y_4', 'Y_5', 'Y_6', 'Y_7', 'Y_8', 'Y_9', 'Y_10', 'Y_11', 'Y_12', 'Y_13', 'Y_14', 'Y_15', 'Y_16', 'Y_17', 'Y_18', 'Y_19', 'Y_20', 'Y_21', 'Y_22', 'Y_23', 'Y_24', 'Y_25', 'Y_26', 'Y_27', 'Y_28', 'Y_29', 'Y_30', 'Y_31', 'Y_32', 'Y_33', 'Y_34', 'Y_35', 'Y_36', 'Y_37', 'Y_38', 'Y_39', 'Y_40', 'Y_41', 'Y_42', 'Y_43', 'Y_44', 'Y_45', 'Y_46', 'Y_47', 'Y_48', 'Y_49', 'Y_50', 'Y_51', 'Y_52', 'Y_53', 'Y_54', 'Y_55', 'Y_56', 'Y_57', 'Y_58', 'Y_59', 'Y_60', 'Y_61', 'Y_62', 'Y_63', 'Y_64', 'Y_65', 'Y_66', 'Y_67', 'Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6', 'Z_7', 'Z_8', 'Z_9', 'Z_10', 'Z_11', 'Z_12', 'Z_13', 'Z_14', 'Z_15', 'Z_16', 'Z_17', 'Z_18', 'Z_19', 'Z_20', 'Z_21', 'Z_22', 'Z_23', 'Z_24', 'Z_25', 'Z_26', 'Z_27', 'Z_28', 'Z_29', 'Z_30', 'Z_31', 'Z_32', 'Z_33', 'Z_34', 'Z_35', 'Z_36', 'Z_37', 'Z_38', 'Z_39', 'Z_40', 'Z_41', 'Z_42', 'Z_43', 'Z_44', 'Z_45', 'Z_46', 'Z_47', 'Z_48', 'Z_49', 'Z_50', 'Z_51', 'Z_52', 'Z_53', 'Z_54', 'Z_55', 'Z_56', 'Z_57', 'Z_58', 'Z_59', 'Z_60', 'Z_61', 'Z_62', 'Z_63', 'Z_64', 'Z_65', 'Z_66', 'Z_67', 'p_scale', 'p_rx', 'p_ry', 'p_rz', 'p_tx', 'p_ty', 'p_0', 'p_1', 'p_2', 'p_3', 'p_4', 'p_5', 'p_6', 'p_7', 'p_8', 'p_9', 'p_10', 'p_11', 'p_12', 'p_13', 'p_14', 'p_15', 'p_16', 'p_17', 'p_18', 'p_19', 'p_20', 'p_21', 'p_22', 'p_23', 'p_24', 'p_25', 'p_26', 'p_27', 'p_28', 'p_29', 'p_30', 'p_31', 'p_32', 'p_33', 'AU01_r', 'AU02_r', 'AU04_r', 'AU05_r', 'AU06_r', 'AU07_r', 'AU09_r', 'AU10_r', 'AU12_r', 'AU14_r', 'AU15_r', 'AU17_r', 'AU20_r', 'AU23_r', 'AU25_r', 'AU26_r', 'AU45_r', 'AU01_c', 'AU02_c', 'AU04_c', 'AU05_c', 'AU06_c', 'AU07_c', 'AU09_c', 'AU10_c', 'AU12_c', 'AU14_c', 'AU15_c', 'AU17_c', 'AU20_c', 'AU23_c', 'AU25_c', 'AU26_c', 'AU28_c', 'AU45_c','Target']
selected_columns = ['gaze_0_x', 'gaze_0_y', 'gaze_0_z','Target']


print("All_columns length"+str(len(all_columns)))
print("selected_columns length"+str(len(selected_columns)))
defaults = [float()]*(len(selected_columns)-1)+[int()]

training_dataset = tf.data.experimental.make_csv_dataset(
    file_pattern="./working_file.csv",
    batch_size=2,
    column_names=all_columns,
    column_defaults=defaults,
    label_name='Target',
    select_columns=selected_columns,
#    field_delim=',',
#    use_quote_delim=True,
    na_value='',
    #header=True,
    #num_epochs=1,
#    shuffle=True,
#    shuffle_buffer_size=1000,
#    shuffle_seed=None,
    prefetch_buffer_size=None,
    num_parallel_reads=None,
    #num_rows_for_inference=100,
    ignore_errors=False)
print(type(training_dataset))



for features in training_dataset.take(2):
    print('Training DS')
    print("features"+str(features))
    print("labels"+str(label))

working_file.csv 的正确输出

<class 'tensorflow.python.data.ops.dataset_ops.PrefetchDataset'>

训练 DS 特征(OrderedDict([('gaze_0_x', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([-0.394292, -0.559326], dtype=float32)>), ('gaze_0_y ', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.158992, 0.098257], dtype=float32)>), ('gaze_0_z', <tf.Tensor: shape=(2, ), dtype=float32, numpy=array([-0.905127, -0.823104], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 1])>) labelstf.Tensor([0. 0.], shape=(2,), dtype=float32) 训练DS特征(OrderedDict([('gaze_0_x', <tf.Tensor: shape=(2,) , dtype=float32, numpy=array([ 0.542843, -0.531369], dtype=float32)>), ('gaze_0_y', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.115069 , 0.19306 ], dtype=float32)>), ('gaze_0_z', <tf.Tensor: shape=(2,), dtype=float32,numpy=array([-0.831914, -0.824848], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 1])>) labelstf .Tensor([0. 0.], shape=(2,), dtype=float32)

file_with_problems.csv 的错误输出

65 def wait(self): 66 """等待在这个执行器中调度的操作完成。""" ---> 67 pywrap_tfe.TFE_ExecutorWaitForAllPendingNodes(self._handle) 68 69 def clear_error(self):

InvalidArgumentError：记录中的字段 3 不是有效的 int32：0.004373601134205451

标签： pythoncsvtensorflow