ijNNddlZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z ddl mZdZed Gd d e Zy) N)backend)ops) keras_export) DataLayer) PyDataset) tensorflowc0t|tr|dS|S)z@Return input from batch; handle (x, y) or (x, y, sample_weight).r) isinstancetuple)batchs /var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/layers/preprocessing/normalization.py_extract_batchrs%Qx Lzkeras.layers.NormalizationcdeZdZdZ d fd ZdZdZdZdZdZ fdZ fd Z d Z d Z xZS) NormalizationaA preprocessing layer that normalizes continuous features. This layer will shift and scale inputs into a distribution centered around 0 with standard deviation 1. It accomplishes this by precomputing the mean and variance of the data, and calling `(input - mean) / sqrt(var)` at runtime. The mean and variance values for the layer must be either supplied on construction or learned via `adapt()`. `adapt()` will compute the mean and variance of the data and store them as the layer's weights. `adapt()` should be called before `fit()`, `evaluate()`, or `predict()`. **Note:** This layer is safe to use inside a `tf.data` or `grain` pipeline (independently of which backend you're using). Args: axis: Integer, tuple of integers, or None. The axis or axes that should have a separate mean and variance for each index in the shape. For example, if shape is `(None, 5)` and `axis=1`, the layer will track 5 separate mean and variance values for the last axis. If `axis` is set to `None`, the layer will normalize all elements in the input by a scalar mean and variance. When `-1`, the last axis of the input is assumed to be a feature dimension and is normalized per index. Note that in the specific case of batched scalar inputs where the only axis is the batch axis, the default will normalize each index in the batch separately. In this case, consider passing `axis=None`. Defaults to `-1`. mean: The mean value(s) to use during normalization. The passed value(s) will be broadcast to the shape of the kept axes above; if the value(s) cannot be broadcast, an error will be raised when this layer's `build()` method is called. `mean` and `variance` must be specified together. variance: The variance value(s) to use during normalization. The passed value(s) will be broadcast to the shape of the kept axes above; if the value(s) cannot be broadcast, an error will be raised when this layer's `build()` method is called. `mean` and `variance` must be specified together. invert: If `True`, this layer will apply the inverse transformation to its inputs: it would turn a normalized input back into its original form. Examples: Calculate a global mean and variance by analyzing the dataset in `adapt()`. >>> adapt_data = np.array([1., 2., 3., 4., 5.], dtype='float32') >>> input_data = np.array([1., 2., 3.], dtype='float32') >>> layer = keras.layers.Normalization(axis=None) >>> layer.adapt(adapt_data) >>> layer(input_data) array([-1.4142135, -0.70710677, 0.], dtype=float32) Calculate a mean and variance for each index on the last axis. >>> adapt_data = np.array([[0., 7., 4.], ... [2., 9., 6.], ... [0., 7., 4.], ... [2., 9., 6.]], dtype='float32') >>> input_data = np.array([[0., 7., 4.]], dtype='float32') >>> layer = keras.layers.Normalization(axis=-1) >>> layer.adapt(adapt_data) >>> layer(input_data) array([-1., -1., -1.], dtype=float32) Pass the mean and variance directly. >>> input_data = np.array([[1.], [2.], [3.]], dtype='float32') >>> layer = keras.layers.Normalization(mean=3., variance=2.) >>> layer(input_data) array([[-1.4142135 ], [-0.70710677], [ 0. ]], dtype=float32) Use the layer to de-normalize inputs (after adapting the layer). >>> adapt_data = np.array([[0., 7., 4.], ... [2., 9., 6.], ... [0., 7., 4.], ... [2., 9., 6.]], dtype='float32') >>> input_data = np.array([[1., 2., 3.]], dtype='float32') >>> layer = keras.layers.Normalization(axis=-1, invert=True) >>> layer.adapt(adapt_data) >>> layer(input_data) array([2., 10., 8.], dtype=float32) c t|di||d}nt|tr|f}n t |}||_||_||_||_d|_ d|_ d|_ |du|duk7rtd|d||tj|tj|k7r7tdtj|dtj|ttj|t|j kDrDtdt|j dtj|d |j yy) NTzUWhen setting values directly, both `mean` and `variance` must be set. Received: mean=z and variance=zcWhen setting values directly, `mean` and `variance` must have the same shape. Received: mean shape z and variance shape zEThe rank of `mean` must be less than or equal to the number of axes (z). Received: mean shape z for axis )super__init__r intr axis input_meaninput_varianceinvertsupports_masking_build_input_shapemean ValueErrornpshapelen)selfrrvariancerkwargs __class__s r rzNormalization.__init__nsj "6" <D c "7D;D & $"&   ($"6 7//3fN8*N   xx~(!33 ""$((4.!12&&(hhx&8%9;288D>"S^3 ''*499~&67""$((4.!1DII;H4 rc : yt _t fdjDrt d djt t jDcgc]}|dk\r|n| zc}_jD]%}| t djdd|t fdt D_ t Dcgc]}|jvrdnd c}_ t Dcgc]}|jvr|nd c}_ t fd jD}|_ jgjd |d d _jd|dd _jdddd d _d_j'yt)j*j}t)j*j,}t)j.|dk(rAt)j0|j}t)j0|j}nd g z}t)j2|}t)j.|}td |d zD]} j| } || || <t)j4||}t)j4||}t)j0|j}t)j0|j}t)j6|j8_t)j6|j8_d_ycc}wcc}wcc}w)Nc38K|]}| kxs|k\ywNr).0andims r z&Normalization.build..s#9!qD5y%AI%9szPAll `axis` values must be in the range [-ndim, ndim). Received inputs with ndim=z , while axis=rzDAll `axis` values to be kept must have a known shape. Received axis=z, inputs.shape=z, with unknown axis at index c3@K|]}|jvs|ywr() _keep_axis)r)dr"s r r,z&Normalization.build..s!" at&>A" sc3(K|] }| ywr(r)r)r/ input_shapes r r,z&Normalization.build..s"Ka;q>"KsrzerosF)namer initializer trainabler#onescountrr)r4r dtyper5r6Tr9)r!ranyrrr sortedr.range _reduce_axis_reduce_axis_mask_broadcast_shape_mean_and_var_shaper add_weight adapt_meanadapt_variancer8builtfinalize_staterconvert_to_tensorrr+ broadcast_tor reshapecast compute_dtyperr#) r"r2r/mean_and_var_shaperr#expanded_shape mean_shape mean_ndimiaxis_idxr+s `` @r buildzNormalization.buildst   ;"- 9tyy9 9--1F- {L  tyyA!aAQX-A B  A1~% %%)YYK0$$/=12236 "" T{"   7?a4??2KN 9! #"K4??"KK#5 ?? ""oo(# .DO #'//(" #2#D # )DJDJ    !((9D,,T-@-@AHxx~"''d.C.CD++Hd6K6KL#$t YYt_ HHTN q)a-0>A#r2H/91"~N8,>{{48;;x@''d.C.CD++Hd6K6KLT-?-?@DIHHXT5G5GHDMDJaB " ! s*N 0NNc N d}t|tjstj|r|j }n)t|t jjr3d}||}t|dk(r|jd}||}nt|trt|dj }nt|drd}t|}t|d}| t!d t|}t#|d d}|"t%d t'|j(d t+|}t-j.|g|}nt%d t'|d|j0s|j3|nA|j4D]2}|||j6|k7st!d|j6d|t|tjrDtj8||j:}tj<||j:} n9tj|rDt?j8||j:}t?j<||j:} nt|t jjtfs|rt?j@|jB}t?j@|jB} d} |D]UttjD|jF|j4D]Z}j |} |j6|} | $| '| | k7s-t!d|j6dt+j dt?j8|j:} t?j<|j:}|j:r+fd|j:D}tIjJ|}nd}| |z } tM|| z }d|z }||z| |zz}| ||z dzz|z|| |z dzz|zz} |}XntOd t'||jPjS||jTjS| |jWy)axComputes the mean and variance of values in a dataset. Calling `adapt()` on a `Normalization` layer is an alternative to passing in `mean` and `variance` arguments during layer construction. A `Normalization` layer should always either be adapted over a dataset or passed `mean` and `variance`. During `adapt()`, the layer will compute a `mean` and `variance` separately for each position in each axis specified by the `axis` argument. To calculate a single `mean` and `variance` over the input data, simply pass `axis=None` to the layer. Arg: data: The data to train on. It can be passed as a NumPy array, a backend-native eager tensor, a `tf.data.Dataset`, a `keras.utils.PyDataset`, or an iterable of batches (e.g. a list of arrays or a generator yielding batches). If a dataset or iterable, *it must be batched*. Keras will assume that each element is a batch, and if that assumption doesn't hold, the mean and variance may be incorrectly computed. Fcr|j}t|tr|dn|}t|jS)Nr) element_specr r r )r/rUx_specs r get_input_shapez,Normalization.adapt..get_input_shapes9 ~~ ",6!O% V\\**rr0r__iter__TNzadapt() received an empty iterable (no batches). Expected at least one batch. Pass a non-empty iterable of arrays or tensors, e.g. layer.adapt([x]) or layer.adapt(list_of_batches).r zadapt() expects an iterable that yields arrays or tensors with a `.shape` attribute (e.g. numpy arrays or backend tensors). Got an element of type zF. Ensure each yielded element is array-like with a `.shape` attribute.zUnsupported data type: z. `adapt` supports `np.ndarray`, backend tensors, `tf.data.Dataset`, `keras.utils.PyDataset`, and iterables of batches (e.g. list, generator).z%The layer was built with input_shape=zO, but adapt() is being called with data with an incompatible shape, data.shape=)rr:z:adapt() yielded a batch with incompatible shape. Expected z, got .c3<K|]}j|ywr()r )r)r/r s r r,z&Normalization.adapt..rs*+, A*sg?),r rndarrayr is_tensorr tfdataDatasetr!r rrhasattriternextrgetattr TypeErrortype__name__r itertoolschainrErRr.rrr>varrr3rArGrKmathprodfloatNotImplementedErrorrCassignrDrF)r"r`data_is_iterabler2rW data_iter first_batchr/ total_mean total_var total_count batch_dimexpected batch_mean batch_varbatch_reduce_shape batch_count batch_weightexisting_weightnew_total_meanr s @r adaptzNormalization.adaptsp,! dBJJ '7+<+T%<%'>&?@==HMK  dBJJ 'D,=,=>Jt$*;*;->?J4+<+<=I rww : ;?O4#;#;"?K"#K{* $[1K? "% "40: 3LLn!< BB#$n!< BB '!! , W+ ,Z&(?T |&LM M z* ""9- rc|j |jsytj|j|j |_tj|j |j|_tj|j|j |_ tj|j|j|_ yr() rrErrIrCr@rrJrKrDr#r"s r rFzNormalization.finalize_states ?? &djj KK1F1FG HHTYY(:(:;  D$7$79N9NO 0B0BC rc |j td|jjj ||j }|j |j}|j |j}|jr|jjj||jjj||jjj|jjj|tjS|jjj|jjj!|||jjj|jjj|tjS)NzAYou must call `.build(input_shape)` on the layer before using it.r:)rrrcorerGrKconvert_weightr#rnumpyaddmultiplymaximumsqrtepsilondividesubtract)r"inputsrr#s r callzNormalization.callsc 99 0 ""44 $,,5  ""499-&&t}}5 ;;<<%%)) ""++LL&&.. **//97??;L <<%%,, ""++FD9 ""**LL&&++H5w7H rc|Sr(r)r"r2s r compute_output_shapez"Normalization.compute_output_shapesrc$t|}|j|j|jt j |jjt j |jjd|S)N)rrrr#) r get_configupdaterrrarrayrtolistr)r"configr%s r rzNormalization.get_configsj#%  ++188:HHT%8%89@@B    rcDt|||jyr()rload_own_variablesrF)r"storer%s r rz Normalization.load_own_variabless "5) rc8|jrd|jiSyNr2)rrs r get_build_configzNormalization.get_build_configs!  " "!4#:#:; ; #rc2|r|j|dyyr)rR)r"rs r build_from_configzNormalization.build_from_configs  JJvm, - r)NNF)rh __module__ __qualname____doc__rrRrrFrrrrrr __classcell__)r%s@r rrsHUp9>)V`DRh D!F  <.rr)rirlrr keras.srcrrkeras.src.api_exportr)keras.src.layers.preprocessing.data_layerr3keras.src.trainers.data_adapters.py_dataset_adapterrkeras.src.utils.module_utilsrr_rrrrr rsI -?I9*+A.IA.,A.r