ijevddlZddlmZddlmZddlmZddlmZddl m Z GddeZ ed d gGd d e Z ed dgGdde Z eddgGdde ZeddgGdde ZeddgGddeZeddgGdd eZed!d"gGd#d$eZed%d&gGd'd(eZed)d*gGd+d,eZed-d.gGd/d0eZd5d1Zegd2Gd3d4e Zy)6N)ops) keras_export)random) Initializer)serialization_libceZdZddZdZy)RandomInitializerNc||_|tj}||_ yt|trt j |}||_ yt|ttjfstd|||_ y)Nzf`seed` argument should be an instance of `keras.random.SeedGenerator()` or an integer. Received: seed=) _init_seedrmake_default_seed isinstancedictrdeserialize_keras_objectint SeedGenerator ValueErrorseed)selfrs /var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/initializers/random_initializers.py__init__zRandomInitializer.__init__ s <++-D d #$==dCD D3(<(<"=>""&)   cHtj|j}d|iS)Nr)rserialize_keras_objectr )r seed_configs r get_configzRandomInitializer.get_configs!'>>tO  $$rN)__name__ __module__ __qualname__rrrrr r s  %rr zkeras.initializers.RandomNormalz keras.initializers.random_normalc6eZdZdZdfd ZddZfdZxZS) RandomNormalaERandom normal initializer. Draws samples from a normal distribution for given parameters. Examples: >>> # Standalone usage: >>> initializer = RandomNormal(mean=0.0, stddev=1.0) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = RandomNormal(mean=0.0, stddev=1.0) >>> layer = Dense(3, kernel_initializer=initializer) Args: mean: A python scalar or a scalar keras tensor. Mean of the random values to generate. stddev: A python scalar or a scalar keras tensor. Standard deviation of the random values to generate. seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. cB||_||_t| |yNrmeanstddevsuperrrr'r(r __class__s rrzRandomNormal.__init__B!   d#rcrtj||j|j|j|SN)shaper'r(rdtype)rnormalr'r(rrr/r0s r__call__zRandomNormal.__call__Gs.}};;   rc^t|}|j|jd}i||SN)r'r(r)rr'r(r base_configconfigr+s rrzRandomNormal.get_configP2g(* ))t{{;(+(((r皙?Nrrrr__doc__rr3r __classcell__r+s@rr"r":$  ))rr"z"keras.initializers.TruncatedNormalz#keras.initializers.truncated_normalc6eZdZdZdfd ZddZfdZxZS)TruncatedNormalaInitializer that generates a truncated normal distribution. The values generated are similar to values from a `RandomNormal` initializer, except that values more than two standard deviations from the mean are discarded and re-drawn. Examples: >>> # Standalone usage: >>> initializer = TruncatedNormal(mean=0., stddev=1.) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = TruncatedNormal(mean=0., stddev=1.) >>> layer = Dense(3, kernel_initializer=initializer) Args: mean: A python scalar or a scalar keras tensor. Mean of the random values to generate. stddev: A python scalar or a scalar keras tensor. Standard deviation of the random values to generate. seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. cB||_||_t| |yr$r&r*s rrzTruncatedNormal.__init__}r,rcrtj||j|j|j|Sr.)rtruncated_normalr'r(rr2s rr3zTruncatedNormal.__call__s0&&;;   rc^t|}|j|jd}i||Sr5r6r7s rrzTruncatedNormal.get_configr:rr;rr>rAs@rrDrDVs@$  ))rrDz keras.initializers.RandomUniformz!keras.initializers.random_uniformc6eZdZdZdfd ZddZfdZxZS) RandomUniformazRandom uniform initializer. Draws samples from a uniform distribution for given parameters. Examples: >>> # Standalone usage: >>> initializer = RandomUniform(minval=0.0, maxval=1.0) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = RandomUniform(minval=0.0, maxval=1.0) >>> layer = Dense(3, kernel_initializer=initializer) Args: minval: A python scalar or a scalar keras tensor. Lower bound of the range of random values to generate (inclusive). maxval: A python scalar or a scalar keras tensor. Upper bound of the range of random values to generate (exclusive). seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. cB||_||_t| |yr$)minvalmaxvalr)r)rrLrMrr+s rrzRandomUniform.__init__s!   d#rcrtj||j|j|j|S)N)r/rLrMrr0)runiformrLrMrr2s rr3zRandomUniform.__call__s.~~;;;;   rc^t|}|j|jd}i||S)N)rLrM)r)rrLrMr7s rrzRandomUniform.get_configs2g(*  KK4;;?(+(((r)gr=Nrr>rAs@rrJrJrBrrJz"keras.initializers.VarianceScalingz#keras.initializers.variance_scalingcBeZdZdZ dfd ZddZfdZxZS)VarianceScalingaInitializer that adapts its scale to the shape of its input tensors. With `distribution="truncated_normal" or "untruncated_normal"`, samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) `stddev = sqrt(scale / n)`, where `n` is: - number of input units in the weight tensor, if `mode="fan_in"` - number of output units, if `mode="fan_out"` - average of the numbers of input and output units, if `mode="fan_avg"` With `distribution="uniform"`, samples are drawn from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(3 * scale / n)`. Examples: >>> # Standalone usage: >>> initializer = VarianceScaling( scale=0.1, mode='fan_in', distribution='uniform') >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = VarianceScaling( scale=0.1, mode='fan_in', distribution='uniform') >>> layer = Dense(3, kernel_initializer=initializer) Args: scale: Scaling factor (positive float). mode: One of `"fan_in"`, `"fan_out"`, `"fan_avg"`. distribution: Random distribution to use. One of `"truncated_normal"`, `"untruncated_normal"`, or `"uniform"`. seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. input_axes: Optional list of integers. The axes of the input tensor that should be considered for computing the fan-in. output_axes: Optional list of integers. The axes of the output tensor that should be considered for computing the fan-out. c$|dkrtd|hd}||vrtd|d||j}|dk(rd}hd}||vrtd |d |||_||_||_||_||_t |!| y) Nr<z9Argument `scale` must be positive float. Received: scale=>fan_infan_avgfan_outzInvalid `mode` argument: z. Please use one of r1rG>rOrGuntruncated_normalz!Invalid `distribution` argument: z.Please use one of r%) rlowerscalemode distribution input_axes output_axesr)r) rrYrZr[rr\r] allowed_modesallowed_distributionsr+s rrzVarianceScaling.__init__s C<##('+ 9 } $+D62%%2O5 $))+ 8 #-L!  4 43L>B%%:$;=   ($& d#rc|j}t||j|j\}}|jdk(r|t d|z}n4|jdk(r|t d|z}n|t d||zdz z}|j dk(r>ufU%dii rct|}|j|j|j|j |j d}i||S)N)rYrZr[r\r])r)rrYrZr[r\r]r7s rrzVarianceScaling.get_config@sPg(* ZZII --//++  )+(((r)rarTrGNNNrr>rAs@rrRrRs2+^ ' &$P6 ) )rrRz keras.initializers.GlorotUniformz!keras.initializers.glorot_uniformc*eZdZdZdfd ZdZxZS) GlorotUniformahThe Glorot uniform initializer, also called Xavier uniform initializer. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(6 / (fan_in + fan_out))` (`fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units). Examples: >>> # Standalone usage: >>> initializer = GlorotUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = GlorotUniform() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html) c0t|ddd|||y)NrarUrOrYrZr[rr\r]r)rrrr\r]r+s rrzGlorotUniform.__init__rs' "!#  rcptj|j|j|jdSN)rr\r]rrr r\r]rs rrzGlorotUniform.get_config|.%<  rrjzkeras.initializers.GlorotNormalz keras.initializers.glorot_normalc*eZdZdZdfd ZdZxZS) GlorotNormala~The Glorot normal initializer, also called Xavier normal initializer. Draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = GlorotNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = GlorotNormal() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html) c0t|ddd|||y)NrarUrGrlrmrns rrzGlorotNormal.__init__s' +!#  rcptj|j|j|jdSrprqrrs rrzGlorotNormal.get_configrsrrtrurAs@rrxrxs@  rrxzkeras.initializers.LecunNormalzkeras.initializers.lecun_normalc*eZdZdZdfd ZdZxZS) LecunNormalaLecun normal initializer. Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized. Draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(1 / fan_in)` where `fan_in` is the number of input units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = LecunNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = LecunNormal() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515) c0t|ddd|||y)NrarTrGrlrmrns rrzLecunNormal.__init__' +!#  rcptj|j|j|jdSrprqrrs rrzLecunNormal.get_configrsrrtrurAs@rr|r|s!F  rr|zkeras.initializers.LecunUniformz keras.initializers.lecun_uniformc*eZdZdZdfd ZdZxZS) LecunUniformaLecun uniform initializer. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(3 / fan_in)` (`fan_in` is the number of input units in the weight tensor). Examples: >>> # Standalone usage: >>> initializer = LecunUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = LecunUniform() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515) c0t|ddd|||y)NrarTrOrlrmrns rrzLecunUniform.__init__' "!#  rcptj|j|j|jdSrprqrrs rrzLecunUniform.get_config)rsrrtrurAs@rrrrvrrzkeras.initializers.HeNormalzkeras.initializers.he_normalc*eZdZdZdfd ZdZxZS)HeNormalaHe normal initializer. It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / fan_in)` where `fan_in` is the number of input units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = HeNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = HeNormal() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [He et al., 2015](https://arxiv.org/abs/1502.01852) c0t|ddd|||y)NrbrTrGrlrmrns rrzHeNormal.__init__Rr~rcptj|j|j|jdSrprqrrs rrzHeNormal.get_config\rsrrtrurAs@rrr1>  rrzkeras.initializers.HeUniformzkeras.initializers.he_uniformc*eZdZdZdfd ZdZxZS) HeUniformaHe uniform variance scaling initializer. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(6 / fan_in)` (`fan_in` is the number of input units in the weight tensor). Examples: >>> # Standalone usage: >>> initializer = HeUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = HeUniform() >>> layer = Dense(3, kernel_initializer=initializer) Args: seed: A Python integer or instance of `keras.backend.SeedGenerator`. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or `None` (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of `keras.backend.SeedGenerator`. Reference: - [He et al., 2015](https://arxiv.org/abs/1502.01852) c0t|ddd|||y)NrbrTrOrlrmrns rrzHeUniform.__init__rrcptj|j|j|jdSrprqrrs rrzHeUniform.get_configrsrrtrurAs@rrrdrrrct|}|:|8d}|D] }|||z} d}|D] }|||z} t|t|fSt|dkrdx}}nPt|dk(r|dx}}n:t|dk(r |d}|d}n!d}|ddD]}||z} |d|z}|d|z}t|t|fS)aZComputes the number of input and output units for a weight shape. Args: shape: Integer shape tuple. input_axes: Optional list of integers, axes representing input units. output_axes: Optional list of integers, axes representing output units. Returns: A tuple of integer scalars: `(fan_in, fan_out)`. Nr)tuplerlen)r/r\r]rTaxisrVreceptive_field_sizedims rrcrcs %LE+"9 "D eDk !F " #D uT{ "G #6{CL(( 5zA~ Uq 8# Uqq( !": (C C '  (r11)22 v;G $$r)zkeras.initializers.Orthogonalzkeras.initializers.orthogonalz(keras.initializers.OrthogonalInitializerc6eZdZdZdfd ZddZfdZxZS) OrthogonalaInitializer that generates an orthogonal matrix. If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns. If the shape of the tensor to initialize is more than two-dimensional, a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])` is initialized, where `n` is the length of the shape vector. The matrix is subsequently reshaped to give a tensor of the desired shape. Examples: >>> # Standalone usage: >>> initializer = keras.initializers.Orthogonal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = keras.initializers.Orthogonal() >>> layer = keras.layers.Dense(3, kernel_initializer=initializer) Args: gain: Multiplicative factor to apply to the orthogonal matrix. seed: A Python integer. Used to make the behavior of the initializer deterministic. Reference: - [Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C) c4||_t| |yr$)gainr)r)rrrr+s rrzOrthogonal.__init__s  d#rct|dkrtd|dt|dd}|ddD]}||z} |d}t||t||f}t j ||j |}tj|\}} tj| } |tj| z}||krtj|}|jtj||zS)NrzKThe tensor to initialize must be at least two-dimensional. Received: shape=z of rank .rr)rr0)rrrdminrr1rrqrdiagsign transposerreshape) rr/r0num_rowsrnum_cols flat_shapeaqrds rr3zOrthogonal.__call__s u:>yU A7 ": C OH 9(H-s8X/FG  MM*499E Bvvay1 HHQK SXXa[ h  a Ayy3;;q%000rcHt|}d|ji}i||S)Nr)r)rrr7s rrzOrthogonal.get_configs.g(* $))$(+(((r)raNrr>rAs@rrrsB$16))rr)NN)re keras.srcrkeras.src.api_exportrkeras.src.backendr"keras.src.initializers.initializerrkeras.src.savingrr r"rDrJrRrjrxr|rrrrcrr rrrs  -$:.% %()* /)$/)  /)d,- 2)'2)  2)j*+ /)%/)  /)d,- z)'z)  z)z*+ / O/  / d)* 0 ?0  0 f() 3 /3  3 l)* / ?/  / d,.LMN/ / O/ d-/NOP/ / Q/ d$%ND)"D)D)r