update doc

Author: shenweichen

docs/source/Model_Methods.md

@@ -33,7 +33,7 @@ Trains the model for a given number of epochs (iterations on a dataset).
 - **verbose**: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
 - **callbacks**: List of `tf.keras.callbacks.Callback` instances. List of callbacks to apply during training and validation (if ). See [callbacks](https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/keras/callbacks).
 - **validation_split**: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling.
-validation_data: tuple `(x_val, y_val)` or tuple `(x_val, y_val, val_sample_weights)` on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. `validation_data` will override `validation_split`.
+- **validation_data**: tuple `(x_val, y_val)` or tuple `(x_val, y_val, val_sample_weights)` on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. `validation_data` will override `validation_split`.
 - **shuffle**: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when `steps_per_epoch` is not `None`.
 - **class_weight**: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
 - **sample_weight**: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`.
@@ -122,7 +122,7 @@ Test the model on a single batch of samples.
 - **x**: Numpy array of test data, or list of Numpy arrays if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to Numpy arrays.
 - **y**: Numpy array of target data, or list of Numpy arrays if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.
 - **sample_weight**: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`.
-- 
+
 **Returns**  
 
 - Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs.

docs/source/index.rst

@@ -34,7 +34,7 @@ You can read the latest code at https://github.com/shenweichen/DeepCTR
 
 News
 -----
-09/08/2019 : Start the development of `DeepCTR-PyTorch <https://github.com/shenweichen/deepctr-pytorch>`_,welcome to join us!! . `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.1>`_
+09/22/2019 : `DeepCTR-Torch <https://github.com/shenweichen/DeepCTR-Torch>`_ first version v0.1.0  is released on `PyPi <https://pypi.org/project/deepctr-torch/>`_ !
 
 08/02/2019 : Now DeepCTR is compatible with tensorflow `1.14` and `2.0.0`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.0>`_