HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

[hathemi]

i hope can someone help me!
here's my code

from six.moves import cPickle
import lasagne
import theano

theano.config.optimizer = "None"

from theano import tensor as T
import numpy as np
import six

import os

os.environ["THEANO_FLAGS"] = "optimizer=None, device=cpu, exception_verbosity=high"
# THEANO_FLAGS = dnn.enabled = False


class CNNModel:
    """Represents a model trained with the Lasagne library."""

    def __init__(self, model_factory, model_weight_path):
        """Loads the CNN model

        Parameters:
            model_factory (module): An object containing a
                    "build_architecture"function.
            model_weights_path (str): A file containing the trained weights
        """
        with open(model_weight_path, "rb") as f:
            if six.PY2:
                model_params = cPickle.load(f)
            else:
                model_params = cPickle.load(f, encoding="latin1")

        self.input_size = model_params["input_size"]
        self.img_size = model_params["img_size"]

        net_input_size = (None, 1, self.input_size[0], self.input_size[1])
        self.model = model_factory.build_architecture(net_input_size, model_params["params"])

        self.forward_util_layer = {}  # Used for caching the functions

    def get_feature_vector(self, image, layer="fc2"):
        """Runs forward propagation until a desired layer, for one input image

        Parameters:
            image (numpy.ndarray): The input image
            layer (str): The desired output layer

        """

        assert len(image.shape) == 2, "Input should have two dimensions: H x W"

        input = image[np.newaxis, np.newaxis]

        # Cache the function that performs forward propagation to the desired layer
        if layer not in self.forward_util_layer:
            inputs = T.tensor4("inputs")
            outputs = lasagne.layers.get_output(self.model[layer], inputs=inputs, deterministic=True)
            self.forward_util_layer[layer] = theano.function([inputs], outputs)

        # Perform forward propagation until the desired layer
        out = self.forward_util_layer[layer](input)
        return out

    def get_feature_vector_multiple(self, images, layer="fc2"):
        """Runs forward propagation until a desired layer, for one input image

        Parameters:
            images (numpy.ndarray): The input images. Should have three dimensions:
                    N x H x W, where N: number of images, H: height, W: width
            layer (str): The desired output layer

        """

        images = np.asarray(images)
        assert len(images.shape) == 3, "Input should have three dimensions: N x H x W"

        # Add the "channel" dimension:
        input = np.expand_dims(images, axis=1)

        # Cache the function that performs forward propagation to the desired layer
        if layer not in self.forward_util_layer:
            inputs = T.tensor4("inputs")
            outputs = lasagne.layers.get_output(self.model[layer], inputs=inputs, deterministic=True)
            self.forward_util_layer[layer] = theano.function([inputs], outputs)

        # Perform forward propagation until the desired layer
        out = self.forward_util_layer[layer](input)
        return out

here's the error log

outputs = lasagne.layers.get_output(self.model[layer], inputs=inputs, deterministic=True)

File "C:\Users\Asus\AppData\Local\Programs\Python\Python39\lib\site-packages\lasagne\layers\helper.py", line 197, in get_output
all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
File "C:\Users\Asus\AppData\Local\Programs\Python\Python39\lib\site-packages\lasagne\layers\conv.py", line 352, in get_output_for
conved = self.convolve(input, **kwargs)
File "C:\Users\Asus\AppData\Local\Programs\Python\Python39\lib\site-packages\lasagne\layers\conv.py", line 645, in convolve
conved = self.convolution(input, self.W,

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.