ValueError: Error when checking input: expected input_1 to have shape (3, 224, 224) but got array with shape (3, 3, 224)

[Nagaraj4896]

_________________________________ test_resnet __________________________________ [gw0] linux -- Python 3.6.7 /home/ironman/anaconda3/envs/Tf_Cv_Ker/bin/python3

def test_resnet():
    app = random.choice(RESNET_LIST)
    module = keras_applications.resnet
    last_dim = 2048

  _test_application_basic(app, module=module)

test_check.py:197:

---

test_check.py:86: in wrapper output = func(*args, **kwargs) test_check.py:150: in _test_application_basic lambda: app(weights='imagenet'), module.preprocess_input) test_check.py:140: in _get_output_shape return (model.output_shape, model.predict(x)) /home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training.py:1441: in predict x, _, _ = self._standardize_user_data(x) /home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training.py:579: in _standardize_user_data exception_prefix='input')

---

data = [array([[[[ 6.4060997e+01, 9.9060997e+01, 1.2106100e+02, ..., 1.1406100e+02, 1.1406100e+02, 1.1406100e+...6.9680000e+01, -6.6800003e+00, ..., 3.4320000e+01, 2.6320000e+01, 5.3320000e+01]]]], dtype=float32)] names = ['input_1'], shapes = [(None, 3, 224, 224)], check_batch_axis = False exception_prefix = 'input'

def standardize_input_data(data,
                           names,
                           shapes=None,
                           check_batch_axis=True,
                           exception_prefix=''):
    """Normalizes inputs and targets provided by users.

    Users may pass data as a list of arrays, dictionary of arrays,
    or as a single array. We normalize this to an ordered list of
    arrays (same order as `names`), while checking that the provided
    arrays have shapes that match the network's expectations.

    # Arguments
        data: User-provided input data (polymorphic).
        names: List of expected array names.
        shapes: Optional list of expected array shapes.
        check_batch_axis: Boolean; whether to check that
            the batch axis of the arrays matches the expected
            value found in `shapes`.
        exception_prefix: String prefix used for exception formatting.

    # Returns
        List of standardized input arrays (one array per model input).

    # Raises
        ValueError: in case of improperly formatted user-provided data.
    """
    if not names:
        if data is not None and hasattr(data, '__len__') and len(data):
            raise ValueError('Error when checking model ' +
                             exception_prefix + ': '
                             'expected no data, but got:', data)
        return []
    if data is None:
        return [None for _ in range(len(names))]

    if isinstance(data, dict):
        try:
            data = [
                data[x].values
                if data[x].__class__.__name__ == 'DataFrame' else data[x]
                for x in names
            ]
        except KeyError as e:
            raise ValueError('No data provided for "' + e.args[0] +
                             '". Need data '
                             'for each key in: ' + str(names))
    elif isinstance(data, list):
        if isinstance(data[0], list):
            data = [np.asarray(d) for d in data]
        elif len(names) == 1 and isinstance(data[0], (float, int)):
            data = [np.asarray(data)]
        else:
            data = [
                x.values if x.__class__.__name__ == 'DataFrame'
                else x for x in data
            ]
    else:
        data = data.values if data.__class__.__name__ == 'DataFrame' else data
        data = [data]
    data = [standardize_single_array(x) for x in data]

    if len(data) != len(names):
        if data and hasattr(data[0], 'shape'):
            raise ValueError(
                'Error when checking model ' + exception_prefix +
                ': the list of Numpy arrays that you are passing to '
                'your model is not the size the model expected. '
                'Expected to see ' + str(len(names)) + ' array(s), '
                'but instead got the following list of ' +
                str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
        elif len(names) > 1:
            raise ValueError(
                'Error when checking model ' + exception_prefix +
                ': you are passing a list as input to your model, '
                'but the model expects a list of ' + str(len(names)) +
                ' Numpy arrays instead. '
                'The list you passed was: ' + str(data)[:200])
        elif len(data) == 1 and not hasattr(data[0], 'shape'):
            raise TypeError('Error when checking model ' + exception_prefix +
                            ': data should be a Numpy array, or list/dict of '
                            'Numpy arrays. Found: ' + str(data)[:200] + '...')
        elif len(names) == 1:
            data = [np.asarray(data)]

    # Check shapes compatibility.
    if shapes:
        for i in range(len(names)):
            if shapes[i] is not None and not K.is_tensor(data[i]):
                data_shape = data[i].shape
                shape = shapes[i]
                if data[i].ndim != len(shape):
                    raise ValueError(
                        'Error when checking ' + exception_prefix +
                        ': expected ' + names[i] + ' to have ' +
                        str(len(shape)) + ' dimensions, but got array '
                        'with shape ' + str(data_shape))
                if not check_batch_axis:
                    data_shape = data_shape[1:]
                    shape = shape[1:]
                for dim, ref_dim in zip(data_shape, shape):
                    if ref_dim != dim and ref_dim:
                        raise ValueError(
                            'Error when checking ' + exception_prefix +
                            ': expected ' + names[i] + ' to have shape ' +
                            str(shape) + ' but got array with shape ' +

                          str(data_shape))

E ValueError: Error when checking input: expected input_1 to have shape (3, 224, 224) but got array with shape (3, 3, 224)

/home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training_utils.py:145: ValueError

[taehoonlee]

@Nagaraj4896, The error is not reproducible. The test _test_application_basic(app, module=module) has been checked in Travis. Are you using tests/data/elephant.jpg?

[Nagaraj4896]

@taehoonlee yes i am using tests/data/elephant.jpg only.