o i e?@sdZddlmmZddlmZddlmZddl m Z ddl m Z ddl mZddl mZdd lmZd Zd Ze Zed d        d!ddZ d"ddZedd#ddZedd$ddZejjdejejd e_ejje_dS)%zInception V3 model for Keras. Reference: - [Rethinking the Inception Architecture for Computer Vision]( http://arxiv.org/abs/1512.00567) (CVPR 2016) N)backend)imagenet_utils)training)VersionAwareLayers) data_utils) layer_utils) keras_exportz|https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5zhttps://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5z+keras.applications.inception_v3.InceptionV3zkeras.applications.InceptionV3TimagenetsoftmaxcCs|dvstjj|std||dkr#|r#|dkr#td|tj|ddt||d}|d ur;t j |d }nt |sHt j ||d }n|}td krSd }nd}t |dddddd} t | ddddd} t | ddd} t j ddd| } t | dd d dd} t | ddddd} t j ddd| } t | dd d } t | dd d } t | ddd} t | dd d } t | ddd} t | ddd} t jdddd| } t | dd d } t j| | | | g|dd} t | dd d } t | dd d } t | ddd} t | dd d } t | ddd} t | ddd} t jdddd| } t | dd d } t j| | | | g|d d} t | dd d } t | dd d } t | ddd} t | dd d } t | ddd} t | ddd} t jdddd| } t | dd d } t j| | | | g|d!d} t | d"ddddd}t | dd d } t | ddd} t | dddddd} t j ddd| } t j|| | g|d#d} t | dd d } t | d$d d }t |d$d d%}t |dd%d }t | d$d d }t |d$d%d }t |d$d d%}t |d$d%d }t |dd d%}t jdddd| } t | dd d } t j| ||| g|d&d} td'D]e}t | dd d } t | d(d d }t |d(d d%}t |dd%d }t | d(d d }t |d(d%d }t |d(d d%}t |d(d%d }t |dd d%}t jdddd| } t | dd d } t j| ||| g|d)td|d} qt | dd d } t | dd d }t |dd d%}t |dd%d }t | dd d }t |dd%d }t |dd d%}t |dd%d }t |dd d%}t jdddd| } t | dd d } t j| ||| g|d*d} t | dd d }t |d+ddddd}t | dd d }t |dd d%}t |dd%d }t |dddddd}t j ddd| } t j||| g|d,d} td'D]u}t | d+d d } t | d"d d }t |d"d d}t |d"dd }t j||g|d-t|d}t | d.d d } t | d"dd} t | d"d d}t | d"dd }t j||g|d/} t jdddd| } t | dd d } t j| || | g|d)td0|d} q|rt jd1d2| } t||t j||d3d4| } n|d5krt | } n |d6krt | } |d urt|}n|}tj|| d7d2}|dkr|rtjd8td9d:d;}n tjda Instantiates the Inception v3 architecture. Reference: - [Rethinking the Inception Architecture for Computer Vision]( http://arxiv.org/abs/1512.00567) (CVPR 2016) This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For `InceptionV3`, call `tf.keras.applications.inception_v3.preprocess_input` on your inputs before passing them to the model. `inception_v3.preprocess_input` will scale input pixels between -1 and 1. Args: include_top: Boolean, whether to include the fully-connected layer at the top, as the last layer of the network. Defaults to `True`. weights: One of `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `imagenet`. input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. `input_tensor` is useful for sharing inputs between multiple different networks. Defaults to `None`. input_shape: Optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(299, 299, 3)` (with `channels_last` data format) or `(3, 299, 299)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. `(150, 150, 3)` would be one valid value. `input_shape` will be ignored if the `input_tensor` is provided. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` (default) means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Defaults to 1000. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. Returns: A `keras.Model` instance. >Nr zThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded; Received: weights=r r zjIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000; Received classes=i+K) default_sizemin_size data_formatrequire_flattenweightsN)shape)tensorrchannels_first )rvalid)stridespadding)r@)rr)rP0`rrsamemixed0)axisnamemixed1mixed2imixed3mixed4rmixedmixed7i@mixed8mixed9_i)r% avg_poolr& predictions) activationr&avgmax inception_v3z2inception_v3_weights_tf_dim_ordering_tf_kernels.h5models 9a0d58056eeedaa3f26cb7ebd46da564) cache_subdir file_hashz8inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 bcbd6486424b2319ff4ef7d526e38f63)tfiogfileexists ValueErrorrobtain_input_shaperimage_data_formatlayersInputis_keras_tensor conv2d_bn MaxPooling2DAveragePooling2D concatenaterangestrGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Drget_source_inputsrModelrget_file WEIGHTS_PATHWEIGHTS_PATH_NO_TOP load_weights) include_topr input_tensor input_shapepoolingclassesclassifier_activation img_input channel_axisx branch1x1 branch5x5 branch3x3dbl branch_pool branch3x3 branch7x7 branch7x7dbli branch7x7x3 branch3x3_1 branch3x3_2branch3x3dbl_1branch3x3dbl_2inputsmodel weights_pathrrR/var/www/myenv/lib/python3.10/site-packages/keras/src/applications/inception_v3.py InceptionV3/sJ                            rtr#r"c Cs|dur |d}|d}nd}d}tdkrd} nd} tj|||f||d|d|}tj| d|d |}tjd |d |}|S) aUtility function to apply conv + BN. Args: x: input tensor. filters: filters in `Conv2D`. num_row: height of the convolution kernel. num_col: width of the convolution kernel. padding: padding mode in `Conv2D`. strides: strides in `Conv2D`. name: name of the ops; will become `name + '_conv'` for the convolution and `name + '_bn'` for the batch norm layer. Returns: Output tensor after applying `Conv2D` and `BatchNormalization`. N_bn_convrrrF)rruse_biasr&)r%scaler&relur4)rrErFConv2DBatchNormalization Activation) rafiltersnum_rownum_colrrr&bn_name conv_namebn_axisrrrrrsrIs*  rIz0keras.applications.inception_v3.preprocess_inputcCstj||ddS)Nr?)rmode)rpreprocess_input)rarrrrrrsrsrz2keras.applications.inception_v3.decode_predictionsr cCstj||dS)N)top)rdecode_predictions)predsrrrrrrsrsr)rreterror)Tr NNNr r )r#r"N)N)r )__doc__tensorflow.compat.v2compatv2r? keras.srcrkeras.src.applicationsrkeras.src.enginerkeras.src.layersrkeras.src.utilsrr tensorflow.python.util.tf_exportrrVrWrFrtrIrrPREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCrrrrrrrssP        d *