Load a model.

Load a parameter.

* \brief Predict the most probable label * \details Returns the argmax of the softmax of the networks output * * \param x Input * \param c

Remove types from function arguments in cython

Changes the number of dimensions. # Examples ``` # use dynet::Dim; let mut dim = Dim::from([2, 3, 4, 5]); assert_eq!(dim.ndims(), 4); // [2, 3, 4, 5

Rewinds the last timestep.

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Samples from the softmax distribution.

Save a lookup parameter.

Save a model.

Save a parameter.

* @brief Scales the gradient (multiplies by `s`) * * @param s scale */

Sets clipping threshold.

Sets the number of nodes in the tree in advance.

Computes sparsemax loss

Returns sum of all dimensions with in a batch. # Examples ``` # use dynet::Dim; let dim = Dim::from([2, 3, 4]); assert_eq!(dim.sum_dims(), 9); // 2

* \brief Whether this node supports processing inputs/outputs on multiple * devices. \details DyNet will throw an error if you try to process input

Copies tensor between devices

Trunsposes the dim. # Panics Panics if the dim has more than two dimensions. # Examples ``` # use dynet::Dim; let d1 = Dim::from([8]); let d1_tran

Truncates trailing dimensions of one. This iterates all the dimensions of `Dim` and stops at last dimension of one. # Examples ``` # use dynet::Dim

* \brief Constructor * * \param m ParameterCollection to be trained * \param eps Bias parameter \f$\epsilon\f$ in the adagrad formula * \p

* \brief Constructor * * \param m ParameterCollection to be trained * \param learning_rate Initial learning rate * \param eps Bias paramet

* \brief Constructor * * \param m ParameterCollection to be trained * \param learning_rate Initial learning rate * \param beta_1 Moving av