B dh @sdZddlmmZddlmZddlmZddl m Z ddl m Z ddl mZedGd d d e Zed dd dZdS)z7Layer that computes the dot product between two inputs.N)backend)base_layer_utils)_Merge)tf_utils) keras_exportzkeras.layers.Dotcs\eZdZdZdfdd ZejddZddZejd d Z dd d Z fddZ Z S)DotaLayer that computes a dot product between samples in two tensors. E.g. if applied to a list of two tensors `a` and `b` of shape `(batch_size, n)`, the output will be a tensor of shape `(batch_size, 1)` where each entry `i` will be the dot product between `a[i]` and `b[i]`. >>> x = np.arange(10).reshape(1, 5, 2) >>> print(x) [[[0 1] [2 3] [4 5] [6 7] [8 9]]] >>> y = np.arange(10, 20).reshape(1, 2, 5) >>> print(y) [[[10 11 12 13 14] [15 16 17 18 19]]] >>> tf.keras.layers.Dot(axes=(1, 2))([x, y]) >>> x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2)) >>> x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2)) >>> dotted = tf.keras.layers.Dot(axes=1)([x1, x2]) >>> dotted.shape TensorShape([5, 1]) Fc stjf|t|tsxt|ttfs4td|t|dkrNtd|t|dtrjt|dtsxtd|||_ ||_ d|_ d|_ d S) aInitializes a layer that computes the element-wise dot product. >>> x = np.arange(10).reshape(1, 5, 2) >>> print(x) [[[0 1] [2 3] [4 5] [6 7] [8 9]]] >>> y = np.arange(10, 20).reshape(1, 2, 5) >>> print(y) [[[10 11 12 13 14] [15 16 17 18 19]]] >>> tf.keras.layers.Dot(axes=(1, 2))([x, y]) Args: axes: Integer or tuple of integers, axis or axes along which to take the dot product. If a tuple, should be two integers corresponding to the desired axis from the first input and the desired axis from the second input, respectively. Note that the size of the two selected axes must match. normalize: Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples. **kwargs: Standard layer keyword arguments. zPInvalid type for argument `axes`: it should be a list or an int. Received: axes=zSInvalid format for argument `axes`: it should contain two elements. Received: axes=rzUInvalid format for argument `axes`: list elements should be integers. Received: axes=TFN) super__init__ isinstanceintlisttuple TypeErrorlen ValueErroraxes normalizeZsupports_maskingZ_reshape_required)selfrrkwargs) __class__J/var/www/html/venv/lib/python3.7/site-packages/keras/layers/merging/dot.pyr ?s     z Dot.__init__c Cst|dtrt|dkr(td||d}|d}|dksH|dkrLdSt|jtr|jdkr|jt||jt|g}q|jgd}n|j}||d||dkrtd||dd|dd||dd|dd|d | dS) NrrzLA `Dot` layer should be called on a list of 2 inputs. Received: input_shape=r z'Incompatible input shapes: axis values z (at axis z) != z). Full input shapes: z, )r rrrrr )r input_shapeshape1shape2rrrrbuildts   z Dot.buildcCst||jt|dkr(td||d}|d}t|jtr~|jdkrp|jt ||jt |g}q|jgd}nZg}xTt t|jD]B}|j|dkr| |j|t ||q| |j|qW|j rt jj||dd}t jj||dd}t|||}|S)NrzEA `Dot` layer should be called on exactly 2 inputs. Received: inputs=rr )Zaxis)rZno_ragged_supportnamerrr rr rndimrangeappendrtfZlinalgZ l2_normalizeZ batch_dot)rinputsx1Zx2rioutputrrr_merge_functions*     zDot._merge_functioncCst|ttfrt|dkr(td|t|d}t|d}t|jtr|jdkrt|jt||jt|g}q|jgd}n|j}||d||d|d||}t|dkr|dg7}t|S)NrzLA `Dot` layer should be called on a list of 2 inputs. Received: input_shape=rr )r rrrrrr pop)rrrrrZ output_shaperrrcompute_output_shapes"        zDot.compute_output_shapeNcCsdS)Nr)rr#maskrrr compute_maskszDot.compute_maskcs4|j|jd}t}tt|t|S)N)rr)rrr get_configdictritems)rconfigZ base_config)rrrr,s  zDot.get_config)F)N) __name__ __module__ __qualname____doc__r rZshape_type_conversionrr'r)r+r, __classcell__rr)rrrs 5 rzkeras.layers.dotFcKstf||d||S)aQFunctional interface to the `Dot` layer. Args: inputs: A list of input tensors (at least 2). axes: Integer or tuple of integers, axis or axes along which to take the dot product. normalize: Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples. **kwargs: Standard layer keyword arguments. Returns: A tensor, the dot product of the samples from the inputs. )rr)r)r#rrrrrrdotsr5)F)r3Ztensorflow.compat.v2compatZv2r"ZkerasrZ keras.enginerZkeras.layers.merging.base_mergerZ keras.utilsrZ tensorflow.python.util.tf_exportrrr5rrrrs     4