Recognizing Handwritten Digits - tutorial - training model step - fails

Options
Herve
Herve Partner, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Dataiku DSS Adv Designer, Registered Posts: 58 Partner
edited July 16 in Academy Discussions 
Traceback (most recent call last):
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 1609, in func_to_apply
    return dic[self.func_name](self.file_reader.read(x))
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/deep_learning/preprocessing.py", line 37, in read
    return folder.get_download_stream(file_id)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/managed_folder.py", line 277, in get_download_stream
    "path" : path
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 422, in jek_or_backend_stream_call
    return backend_stream_call(path, data, err_msg, **kwargs)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 413, in backend_stream_call
    return _handle_stream_resp(backend_api_post_call(path, data, stream=True, **kwargs), err_msg = err_msg)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 469, in _handle_stream_resp
    raise Exception("%s: %s" % (err_msg, _get_error_message(err_data).encode("utf8")))
Exception: None: b'No such file in this managed folder.'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/server.py", line 46, in serve
    ret = api_command(arg)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/dkuapi.py", line 45, in aux
    return api(**kwargs)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/commands.py", line 456, in train_prediction_keras
    pipeline.generated_features_mapping)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/prediction_entrypoints.py", line 441, in prediction_train_model_keras
    generated_features_mapping)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/deep_learning/keras_support.py", line 126, in get_keras_model
    shuffle=keras_params["shuffleData"])
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/engine/training.py", line 1732, in fit_generator
    initial_epoch=initial_epoch)
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/engine/training_generator.py", line 185, in fit_generator
    generator_output = next(output_generator)
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/utils/data_utils.py", line 625, in get
    six.reraise(*sys.exc_info())
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/six.py", line 703, in reraise
    raise value
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/utils/data_utils.py", line 610, in get
    inputs = future.get(timeout=30)
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 644, in get
    raise self._value
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/keras/utils/data_utils.py", line 406, in get_index
    return _SHARED_SEQUENCES[uid][i]
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/deep_learning/sequences.py", line 131, in __getitem__
    batch = self.__get_batch_from_array(index_array)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/deep_learning/sequences.py", line 160, in __get_batch_from_array
    batch_transformed = self.pipeline.process(batch_input_df)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 1958, in process
    new_mf = step.process(input_df, cur_mf, result, self.generated_features_mapping)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 1597, in process
    blk = np.array(serie.apply(self.__apply_user_defined_func(dic)).tolist())
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/pandas/core/series.py", line 3848, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/lib.pyx", line 2329, in pandas._libs.lib.map_infer
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 1609, in func_to_apply
    return dic[self.func_name](self.file_reader.read(x))
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/base/utils.py", line 176, in __exit__
    exc_tb)
  File "/home/hblanc/DATA_DIR/code-envs/python/PYTHON36/lib/python3.6/site-packages/six.py", line 702, in reraise
    raise value.with_traceback(tb)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 1609, in func_to_apply
    return dic[self.func_name](self.file_reader.read(x))
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/doctor/deep_learning/preprocessing.py", line 37, in read
    return folder.get_download_stream(file_id)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/managed_folder.py", line 277, in get_download_stream
    "path" : path
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 422, in jek_or_backend_stream_call
    return backend_stream_call(path, data, err_msg, **kwargs)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 413, in backend_stream_call
    return _handle_stream_resp(backend_api_post_call(path, data, stream=True, **kwargs), err_msg = err_msg)
  File "/home/hblanc/dataiku-dss-8.0.5/python/dataiku/core/intercom.py", line 469, in _handle_stream_resp
    raise Exception("%s: %s" % (err_msg, _get_error_message(err_data).encode("utf8")))
Exception: Failed to preprocess the following file: 'training/2/57890.png', Error: None: b'No such file in this managed folder.'
Tagged:
· Share on FacebookShare on Twitter

Best Answer

Answers

  • Herve
    Herve Partner, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Dataiku DSS Adv Designer, Registered Posts: 58 Partner
    Options

    from keras.layers import Conv2D, MaxPooling2D, Input
    from keras.layers import Dense, BatchNormalization, Activation
    from keras.layers import Flatten, Dropout
    from keras.models import Model
    from keras.optimizers import Adam

    def build_model(input_shapes, n_classes=None):

    #### DEFINING INPUT AND BASE ARCHITECTURE
    # You need to modify the name and shape of the "image_input"
    # according to the preprocessing and name of your
    # initial feature.
    # This feature should to be preprocessed as an "Image", with a
    # custom preprocessing.
    image_shape = (28, 28, 3)
    image_input_name = "path_preprocessed"

    image_input = Input(shape=image_shape, name=image_input_name)

    #### DEFINING THE ARCHITECTURE
    x = Conv2D(32, kernel_size=3, padding='same', activation='relu')(image_input)
    x = Conv2D(32, kernel_size=3, padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.2)(x)
    predictions = Dense(n_classes, activation='softmax')(x)

    model = Model(inputs=image_input, outputs=predictions)
    return model

    def compile_model(model):
    model.compile(
    optimizer="adam",
    loss="categorical_crossentropy"
    )
    return model

    · Share on FacebookShare on Twitter
  • Herve
    Herve Partner, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Dataiku DSS Adv Designer, Registered Posts: 58 Partner
    Options

    from keras.preprocessing.image import img_to_array, load_img

    # Custom image preprocessing function.
    # Must return a numpy ndarray representing the image.
    # - image_file is a file like object
    def preprocess_image(image_file):
    img = load_img(image_file,target_size=(28, 28, 3))
    array = img_to_array(img)

    # Define the actual preprocessing here
    array = array / 255
    return array

    · Share on FacebookShare on Twitter
  • Alex_Reutter
    Alex_Reutter Alpha Tester, Dataiker Alumni, Dataiku DSS Core Designer Posts: 105 ✭✭✭✭✭✭✭
    edited July 17
    Options

    Hi @Herve
    , the line

    Exception: Failed to preprocess the following file: 'training/2/57890.png', Error: None: b'No such file in this managed folder.'

    ...suggests that DSS can't find the file during preprocessing. Can you confirm that the file is in the folder selected in the Features handling panel of the design of the deep learning model?

    Thanks,

    Alex

    · Share on FacebookShare on Twitter
  • Herve
    Herve Partner, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Dataiku DSS Adv Designer, Registered Posts: 58 Partner
    Options

    File is there,

    handwritten training 2 57890.png

     

    yes

    · Share on FacebookShare on Twitter
  • Herve
    Herve Partner, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Dataiku DSS Adv Designer, Registered Posts: 58 Partner
    Options

    handwritten feature handling.png

     

    · Share on FacebookShare on Twitter
Bookmark this topic

Categories

Setup Info
    Tags
      Help me…