buiding a network using lasagne and theano

Question

I am trying to build a network but I am getting the following error

this is the building and training code

The network was built by this following video code

The data I created using this repository githup repository

The train contains 24 class of images and the test contains 24 class of labels

import os
import numpy as np
import lasagne
import theano
import theano.tensor as T


def load_mnist_images(filename):
    with gzip.open(filename,'rb') as f :
        data = np.frombuffer(f.read(),np.uint8,offset=16)
        data = data.reshape(-1,1,28,28)
        print(type(data))
        return data/np.float32(256)

def load_mnist_labels(filename):
    with gzip.open(filename,'rb')as f:
        data=np.frombuffer(f.read(),np.uint8,offset=8)
        return data

def load_dataset():
    x_train = load_mnist_images('train-images-idx3-ubyte.gz')
    y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')
    x_test = load_mnist_images('test-images-idx3-ubyte.gz')
    y_test = load_mnist_labels('test-labels-idx1-ubyte.gz')

    return x_train,y_train,x_test,y_test

def build_nn(input_var=None):

    l_in = lasagne.layers.InputLayer(shape=(None,1,28,28),input_var=input_var)

    l_in_drop = lasagne.layers.DropoutLayer(l_in,p=0.2)

    l_hid1 = lasagne.layers.DenseLayer(l_in_drop,num_units=800,
                                       nonlinearity=lasagne.nonlinearities.rectify,
                                       W=lasagne.init.GlorotUniform())

    l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1,p=0.5)

    l_hid2 = lasagne.layers.DenseLayer(l_hid1_drop, num_units=800,
                                       nonlinearity=lasagne.nonlinearities.rectify,
                                       W=lasagne.init.GlorotUniform())

    l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)

    l_out = lasagne.layers.DenseLayer(l_hid2_drop,num_units=24 ,
                                      nonlinearity= lasagne.nonlinearities.softmax)

    return l_out
if __name__ == "__main__":
    x_train,y_train,x_test,y_test = load_dataset()

    input_var = T.tensor4('inputs')
    target_var = T.tensor4('targets')
    print(target_var)
    network = build_nn(input_var)

    prediction = lasagne.layers.get_output(network)

    loss = lasagne.objectives.categorical_crossentropy(prediction,target_var)

    loss = loss.mean()

    params = lasagne.layers.get_all_params(network, trainable=True)

    updates = lasagne.updates.nesterov_momentum(loss,params,learning_rate=0.01 , momentum=0.9)

    train_fn = theano.function([input_var,target_var],loss , updates=updates)

    num_training_steps = 10

    for steps in range(num_training_steps):
        train_err = train_fn(x_train,y_train)
        print("current step is " + str(steps))

and this is the error i get

Traceback (most recent call last):
  File "/home/hassan/JPG-PNG-to-MNIST-NN-Format/mnist_test.py", line 64, in <module>
    loss = lasagne.objectives.categorical_crossentropy(prediction,target_var)
  File "/home/hassan/anaconda3/envs/object-detection/lib/python3.7/site-packages/lasagne/objectives.py", line 181, in categorical_crossentropy
    return theano.tensor.nnet.categorical_crossentropy(predictions, targets)
  File "/home/hassan/anaconda3/envs/object-detection/lib/python3.7/site-packages/theano/tensor/nnet/nnet.py", line 2101, in categorical_crossentropy
    raise TypeError('rank mismatch between coding and true distributions')
TypeError: rank mismatch between coding and true distributions

Answer 1

I solved it there was a mistake in writing te code

instead of writing this

target_var = T.ivector('targets')

I wrote this

target_var = T.tensor4('targets')