deepxde.backend.pytorch package

Submodules

deepxde.backend.pytorch.tensor module

pytorch backend implementation

deepxde.backend.pytorch.tensor.Variable(initial_value, dtype=None)[source]
deepxde.backend.pytorch.tensor.abs(x)[source]
deepxde.backend.pytorch.tensor.as_tensor(data, dtype=None)[source]
deepxde.backend.pytorch.tensor.concat(values, axis)[source]
deepxde.backend.pytorch.tensor.cos(x)[source]
deepxde.backend.pytorch.tensor.data_type_dict()[source]
deepxde.backend.pytorch.tensor.elu(x)[source]
deepxde.backend.pytorch.tensor.exp(x)[source]
deepxde.backend.pytorch.tensor.expand_dims(tensor, axis)[source]
deepxde.backend.pytorch.tensor.from_numpy(np_array)[source]
deepxde.backend.pytorch.tensor.gelu(x)[source]
deepxde.backend.pytorch.tensor.is_gpu_available()[source]
deepxde.backend.pytorch.tensor.is_tensor(obj)[source]
deepxde.backend.pytorch.tensor.lgamma(x)[source]
deepxde.backend.pytorch.tensor.matmul(x, y)[source]
deepxde.backend.pytorch.tensor.max(input_tensor, dim, keepdims=False)[source]
deepxde.backend.pytorch.tensor.mean(input_tensor, dim, keepdims=False)[source]
deepxde.backend.pytorch.tensor.min(input_tensor, dim, keepdims=False)[source]
deepxde.backend.pytorch.tensor.minimum(x, y)[source]
deepxde.backend.pytorch.tensor.ndim(input_tensor)[source]
deepxde.backend.pytorch.tensor.norm(tensor, ord=None, axis=None, keepdims=False)[source]
deepxde.backend.pytorch.tensor.pow(x, y)[source]
deepxde.backend.pytorch.tensor.prod(input_tensor, dim, keepdims=False)[source]
deepxde.backend.pytorch.tensor.reduce_max(input_tensor)[source]
deepxde.backend.pytorch.tensor.reduce_mean(input_tensor)[source]
deepxde.backend.pytorch.tensor.reduce_min(input_tensor)[source]
deepxde.backend.pytorch.tensor.reduce_prod(input_tensor)[source]
deepxde.backend.pytorch.tensor.reduce_sum(input_tensor)[source]
deepxde.backend.pytorch.tensor.relu(x)[source]
deepxde.backend.pytorch.tensor.reshape(tensor, shape)[source]
deepxde.backend.pytorch.tensor.reverse(tensor, axis)[source]
deepxde.backend.pytorch.tensor.roll(tensor, shift, axis)[source]
deepxde.backend.pytorch.tensor.selu(x)[source]
deepxde.backend.pytorch.tensor.shape(input_tensor)[source]
deepxde.backend.pytorch.tensor.sigmoid(x)[source]
deepxde.backend.pytorch.tensor.silu(x)[source]
deepxde.backend.pytorch.tensor.sin(x)[source]
deepxde.backend.pytorch.tensor.size(tensor)[source]
deepxde.backend.pytorch.tensor.sparse_dense_matmul(x, y)[source]
deepxde.backend.pytorch.tensor.sparse_tensor(indices, values, shape)[source]
deepxde.backend.pytorch.tensor.square(x)[source]
deepxde.backend.pytorch.tensor.stack(values, axis)[source]
deepxde.backend.pytorch.tensor.sum(input_tensor, dim, keepdims=False)[source]
deepxde.backend.pytorch.tensor.tanh(x)[source]
deepxde.backend.pytorch.tensor.to_numpy(input_tensor)[source]
deepxde.backend.pytorch.tensor.transpose(tensor, axes=None)[source]
deepxde.backend.pytorch.tensor.zeros(shape, dtype)[source]
deepxde.backend.pytorch.tensor.zeros_like(input_tensor)[source]

Module contents