6bim.SrSSKJs Jr SSKJr SSKJr SSKJr SSKJ r SSK J r SSK J r SS KJr SS KJr SS KJr \"S S 5"SS\55r\rg)zGKeras 1D transposed convolution layer (sometimes called deconvolution).N) activations) constraints) initializers) regularizers)utils) InputSpec)Conv1D) conv_utils) keras_exportzkeras.layers.Conv1DTransposez#keras.layers.Convolution1DTransposec^\rSrSrSr\R S U4Sjj5rSrSr Sr U4Sjr Sr U=r $) Conv1DTranspose!aTransposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers or `None`, does not include the sample axis), e.g. `input_shape=(128, 3)` for data with 128 time steps and 3 channels. Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer length of the 1D convolution window. strides: An integer specifying the stride of the convolution along the time dimension. Specifying a stride value != 1 is incompatible with specifying a `dilation_rate` value != 1. Defaults to `1`. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. output_padding: An integer specifying the amount of padding along the time dimension of the output tensor. The amount of output padding must be lower than the stride. If set to `None` (default), the output shape is inferred. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch_size, length, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, channels, length)`. dilation_rate: an integer, specifying the dilation rate to use for dilated convolution. Currently, specifying a `dilation_rate` value != 1 is incompatible with specifying a stride value != 1. Also dilation rate larger than 1 is not currently supported. activation: Activation function to use. If you don't specify anything, no activation is applied (see `keras.activations`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix (see `keras.initializers`). Defaults to 'glorot_uniform'. bias_initializer: Initializer for the bias vector (see `keras.initializers`). Defaults to 'zeros'. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see `keras.regularizers`). bias_regularizer: Regularizer function applied to the bias vector (see `keras.regularizers`). activity_regularizer: Regularizer function applied to the output of the layer (its "activation") (see `keras.regularizers`). kernel_constraint: Constraint function applied to the kernel matrix (see `keras.constraints`). bias_constraint: Constraint function applied to the bias vector (see `keras.constraints`). Input shape: 3D tensor with shape: `(batch_size, steps, channels)` Output shape: 3D tensor with shape: `(batch_size, new_steps, filters)` If `output_padding` is specified: ``` new_timesteps = ((timesteps - 1) * strides + kernel_size - 2 * padding + output_padding) ``` Returns: A tensor of rank 3 representing `activation(conv1dtranspose(inputs, kernel) + bias)`. Raises: ValueError: if `padding` is "causal". ValueError: when both `strides` > 1 and `dilation_rate` > 1. References: - [A guide to convolution arithmetic for deep learning]( https://arxiv.org/abs/1603.07285v1) - [Deconvolutional Networks]( https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf) c >[TU]"S UUUUUU[R"U5U [R"U 5[R"U 5[ R"U 5[ R"U 5[ R"U5[ R"U5[ R"U5S.UD6 XPlURb}[R"URSSSS9Ul[URUR5H2unnUU:dM[SURSURS35e gg) N)filters kernel_sizestridespadding data_format dilation_rate activationuse_biaskernel_initializerbias_initializerkernel_regularizerbias_regularizeractivity_regularizerkernel_constraintbias_constraintoutput_paddingT) allow_zeroz>Strides must be greater than output padding. Received strides=z, output_padding=.) super__init__rgetrrrr r normalize_tuplezipr ValueError)selfrrrrr rrrrrrrrrrrkwargsstrideout_pad __class__s l/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/convolutional/conv1d_transpose.pyr%Conv1DTranspose.__init__{s@*  ##'"z2+//0BC)--.>?+//0BC)--.>?!-!1!12F!G)oo.?@'OOO< ! &-    *","<"<##Q(8T#D $'t||T5H5H#If$$,,0LL>:**.*=*=)>aA$J +c [R"U5n[U5S:wa[SUS35eUR 5nUR UR c[SUSUS35e[X5n[SX#0S9Ul URURU4-nURSUURURURS UR S 9UlUR$(aNURS UR4UR&UR(UR*S UR S 9UlOSUlS Ulg) Nz0Inputs should have rank 3. Received input_shape=r"zhThe channel dimension of the inputs to `Conv1DTranspose` should be defined. The input_shape received is z , where axis z> (0-based) is the channel dimension, which found to be `None`.)ndimaxeskernelT)nameshape initializer regularizer constraint trainabledtypebias)tf TensorShapelenr)_get_channel_axisdimsvalueintr input_specrr add_weightrrrr=r6rrrrr>built)r* input_shape channel_axis input_dim kernel_shapes r/buildConv1DTranspose.buildsgnn[1 { q ((3}A7 --/   L ) / / 7//:m<*^,FF  12 #,1JK''4<<*CC oo////--**&   ==||o 11 11//jj(DIDI r1c &[R"U5nUSnURS:XaSnOSnX$nURcSnOURSn[R "UUR SURUURSURSS9nURS:XaX0RU4nOX7UR4n[R"URSS9n [R"U5n [RRUURU URURR!5U URS9n [R""5(dGURR$(a,UR'UR5n U R)U 5 UR*(a([RR-XR.U S 9n UR0bUR1U 5$U $) Nrchannels_firstrrr r,dilationr3)r4)rrr dilations)r)r?r8rr r deconv_output_lengthrrrrrconvert_data_formatstacknnconv1d_transposer6upperexecuting_eagerlyrankcompute_output_shape set_shaperbias_addr>r) r*inputs inputs_shape batch_sizet_axislengthr out_length output_shaperoutput_shape_tensoroutputs out_shapes r/callConv1DTranspose.callsxx' !!_   / /FF%    &!N!003N 44    Q LL)<<?''*     / /& jAL&DLLAL 44T5E5EAN  hh|4%%((  KK LLLL&&(#(() ##%%&,,*;*;11&,,?I   i ( ==eenn %G ?? &??7+ +r1c [R"U5R5n[U5nURS:XaSup4OSup4UR cSnOUR SnUR X#'[R"X$URSURUURSURSS9X$'[R"U5$)NrP)rrQ)rQrrrR) r?r@as_listlistrr rr rUrrrr)r*rIrfc_axisrcr s r/r]$Conv1DTranspose.compute_output_shapesnn[199; K(   / /!NFF!NF    &!N!003N#|| )>>    Q LL)<<?''*  ~~l++r1cB>[TU]5nURUS'U$)Nr )r$ get_configr )r*configr.s r/rrConv1DTranspose.get_config(s&#%#'#6#6  r1)r>rHrFr6r )rvalidNNrNTglorot_uniformzerosNNNNN)__name__ __module__ __qualname____firstlineno____doc__rallow_initializer_layoutr%rMrjr]rr__static_attributes__ __classcell__)r.s@r/r r !slTl ## + !#2$2h)V4l,.r1r )r|tensorflow.compat.v2compatv2r? tf_keras.srcrrrrtf_keras.src.dtensorrtf_keras.src.engine.input_specr(tf_keras.src.layers.convolutional.conv1dr tf_keras.src.utilsr tensorflow.python.util.tf_exportr r Convolution1DTransposer#r1r/rsbN"!$$%%&4;):"$IGfGGX)r1