dkn22 / embedder Goto Github PK

X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=2001)
xgb_train = xgboost.DMatrix(X_train, label=y_train)
xgb_test = xgboost.DMatrix(X_test, label=y_test)

eval_set = [(X_train, y_train), (X_test, y_test)]
xgb_model.fit(X_train, y_train, model__eval_set=eval_set, model__verbose=True,
              model__early_stopping_rounds=50);

from embedder.preprocessing import (categorize,pick_emb_dim, encode_categorical)
categorical_variable_count = categorize(X)
dict_embedding_size = pick_emb_dim(categorical_variable_count, max_dim=50)
X_encoded, encoders = encode_categorical(X)
embedder = Embedder(dict_embedding_size, model_json=None)
embedder.fit(X_encoded, y)

---------------------------------------------------------------------------
MXNetError                                Traceback (most recent call last)
<ipython-input-11-0ccee6a2b185> in <module>
      5 from embedder.classification import Embedder
      6 embedder = Embedder(dict_embedding_size, model_json=None)
----> 7 embedder.fit(X_encoded, y)
      8 
      9 print('PreProcessing time: '+ str(datetime.now() - start_time))

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/embedder/classification.py in fit(self, X, y, batch_size, epochs, checkpoint, early_stop)
     31         '''
     32 
---> 33         nnet = self._create_model(X, model_json=self.model_json)
     34 
     35         nnet.compile(loss='binary_crossentropy',

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/embedder/base.py in _create_model(self, X, model_json)
     85         if model_json is None:
     86             if hasattr(self, '_default_nnet'):
---> 87                 nnet = self._default_nnet(X)
     88             else:
     89                 raise ValueError('No model architecture provided.')

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/embedder/classification.py in _default_nnet(self, X)
     97         flatten = concatenate(flatten_layers, axis=-1)
     98 
---> 99         fc1 = Dense(1000, kernel_initializer='normal')(flatten)
    100         fc1 = Activation('relu')(fc1)
    101         # fc1 = BatchNormalization(fc1)

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
    468             # Actually call the layer,
    469             # collecting output(s), mask(s), and shape(s).
--> 470             output = self.call(inputs, **kwargs)
    471             output_mask = self.compute_mask(inputs, previous_mask)
    472 

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/layers/core.py in call(self, inputs)
    891         output = K.dot(inputs, self.kernel)
    892         if self.use_bias:
--> 893             output = K.bias_add(output, self.bias, data_format='channels_last')
    894         if self.activation is not None:
    895             output = self.activation(output)

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/backend/mxnet_backend.py in func_wrapper(*args, **kwargs)
     92                 # Create Train Symbol
     93                 set_learning_phase(1)
---> 94                 train_symbol = func(*args, **kwargs)
     95                 # Create Test Symbol
     96                 set_learning_phase(0)

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/backend/mxnet_backend.py in bias_add(x, bias, data_format)
   3980         raise ValueError('MXNet Backend: Unknown data_format ' + str(data_format))
   3981     bias_shape = int_shape(bias)
-> 3982     x_dim = ndim(x)
   3983     if len(bias_shape) != 1 and len(bias_shape) != x_dim - 1:
   3984         raise ValueError('MXNet Backend: Unexpected bias dimensions %d, expect to be 1 or %d dimensions'

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/backend/mxnet_backend.py in ndim(x)
    533     ```
    534     """
--> 535     shape = x.shape
    536     if shape is not None:
    537         return len(shape)

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/backend/mxnet_backend.py in shape(self)
   4393     @property
   4394     def shape(self):
-> 4395         return self._get_shape()
   4396 
   4397     def eval(self):

~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/keras/backend/mxnet_backend.py in _get_shape(self)
   4402             return self._keras_shape
   4403         else:
-> 4404             _, out_shape, _ = self.symbol.infer_shape_partial()
   4405             return out_shape[0]
   4406 

~/anaconda3/envs/mxnet_latest_p37/cpu/lib/python3.7/site-packages/mxnet/symbol/symbol.py in infer_shape_partial(self, *args, **kwargs)
   1175             The order is same as the order of list_auxiliary_states().
   1176         """
-> 1177         return self._infer_shape_impl(True, *args, **kwargs)
   1178 
   1179     def _infer_shape_impl(self, partial, *args, **kwargs):

~/anaconda3/envs/mxnet_latest_p37/cpu/lib/python3.7/site-packages/mxnet/symbol/symbol.py in _infer_shape_impl(self, partial, *args, **kwargs)
   1263                 ctypes.byref(aux_shape_ndim),
   1264                 ctypes.byref(aux_shape_data),
-> 1265                 ctypes.byref(complete)))
   1266         if complete.value != 0:
   1267             arg_shapes = [tuple(arg_shape_data[i][:arg_shape_ndim[i]])

~/anaconda3/envs/mxnet_latest_p37/cpu/lib/python3.7/site-packages/mxnet/base.py in check_call(ret)
    244     """
    245     if ret != 0:
--> 246         raise get_last_ffi_error()
    247 
    248 

MXNetError: MXNetError: Error in operator dot0: [19:57:48] src/operator/tensor/./dot-inl.h:1241: Check failed: L[!Ta].Size() == R[Tb].Size() (76 vs. 292) : dot shape error: [-1,76] X [292,1000]