model initiating error

[pranavpawar3]

Hi, I am implementing the model on MovieLens dataset, I am facing an issue with model training. When I start training on the dataset, it generates the following error,

`InvalidType: Invalid operation is performed in: LinearFunction (Forward)

Expect: x.shape[1] == W.shape[1] Actual: 5 != 950198`

The complete output of model.fit(X,y) is as follows:

`load dataset The number of data, train: 950198 validate: 50011 prepare initialized model!

0%| | 0/5000 [00:00<?, ?it/s]

---

InvalidType Traceback (most recent call last) in ----> 1 model.fit(X,y)

C:/Users/ppawar/Desktop/Genesys_PDP_code/ml-1m/learning2rank/rank\RankNet.py in fit(self, fit_X, fit_y, batchsize, n_iter, n_units1, n_units2, tv_ratio, optimizerAlgorithm, savefigName, savemodelName) 131 self.initializeModel(Model, train_X, n_units1, n_units2, optimizerAlgorithm) 132 --> 133 self.trainModel(train_X, train_y, validate_X, validate_y, n_iter) 134 135 plot_result.acc(self.train_loss, self.test_loss, savename=savefigName)

C:/Users/ppawar/Desktop/Genesys_PDP_code/ml-1m/learning2rank/rank\RankNet.py in trainModel(self, x_train, y_train, x_test, y_test, n_iter) 111 y_j = chainer.Variable(y_train[j]) 112 --> 113 self.optimizer.update(self.model, x_i, x_j, y_i, y_j) 114 115 if (step + 1) % loss_step == 0:

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\optimizer.py in update(self, lossfun, *args, **kwds) 678 if lossfun is not None: 679 use_cleargrads = getattr(self, '_use_cleargrads', True) --> 680 loss = lossfun(*args, **kwds) 681 if use_cleargrads: 682 self.target.cleargrads()

C:/Users/ppawar/Desktop/Genesys_PDP_code/ml-1m/learning2rank/rank\RankNet.py in call(self, x_i, x_j, t_i, t_j) 35 ) 36 def call(self, x_i, x_j, t_i, t_j): ---> 37 s_i = self.l3(F.relu(self.l2(F.relu(self.l1(x_i))))) 38 s_j = self.l3(F.relu(self.l2(F.relu(self.l1(x_j))))) 39 s_diff = s_i - s_j

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\link.py in call(self, *args, **kwargs) 240 if forward is None: 241 forward = self.forward --> 242 out = forward(*args, **kwargs) 243 244 # Call forward_postprocess hook

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\links\connection\linear.py in forward(self, x, n_batch_axes) 136 in_size = functools.reduce(operator.mul, x.shape[1:], 1) 137 self._initialize_params(in_size) --> 138 return linear.linear(x, self.W, self.b, n_batch_axes=n_batch_axes)

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\functions\connection\linear.py in linear(x, W, b, n_batch_axes) 286 args = x, W, b 287 --> 288 y, = LinearFunction().apply(args) 289 if n_batch_axes > 1: 290 y = y.reshape(batch_shape + (-1,))

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\function_node.py in apply(self, inputs) 243 244 if configuration.config.type_check: --> 245 self._check_data_type_forward(in_data) 246 247 hooks = chainer.get_function_hooks()

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\function_node.py in _check_data_type_forward(self, in_data) 328 in_type = type_check.get_types(in_data, 'in_types', False) 329 with type_check.get_function_check_context(self): --> 330 self.check_type_forward(in_type) 331 332 def check_type_forward(self, in_types):

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\functions\connection\linear.py in check_type_forward(self, in_types) 25 x_type.ndim == 2, 26 w_type.ndim == 2, ---> 27 x_type.shape[1] == w_type.shape[1], 28 ) 29 if type_check.eval(n_in) == 3:

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\utils\type_check.py in expect(*bool_exprs) 544 for expr in bool_exprs: 545 assert isinstance(expr, Testable) --> 546 expr.expect() 547 548

~\AppData\Local\Continuum\anaconda3\envs\tensorflow_gpu_keras\lib\site-packages\chainer\utils\type_check.py in expect(self) 481 raise InvalidType( 482 '{0} {1} {2}'.format(self.lhs, self.exp, self.rhs), --> 483 '{0} {1} {2}'.format(left, self.inv, right)) 484 485

InvalidType: Invalid operation is performed in: LinearFunction (Forward)

Expect: x.shape[1] == W.shape[1] Actual: 5 != 950198 `