ó ¾÷Xc@@s8ddlmZddlmZddlmZddlmZddlm Z ddl m Z defd „ƒYZ d e fd „ƒYZ d e fd „ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZd efd!„ƒYZd"efd#„ƒYZd$efd%„ƒYZd&efd'„ƒYZd(efd)„ƒYZd*efd+„ƒYZeZe ZeZ eZ!eZ"eZ#eZ$eZ%eZ&eZ'eZ(eZ)d,S(-i(tabsolute_importi(tbackend(tLayer(t InputSpec(t conv_utils(t interfacest _Pooling1DcB@sDeZdZdddd„Zd„Zd„Zd„Zd„ZRS( s4Abstract class for different pooling 1D layers. itvalidcK@sƒtt|ƒj||dkr+|}ntj|ddƒ|_tj|ddƒ|_tj|ƒ|_ t ddƒ|_ dS(Nit pool_sizetstridestndimi( tsuperRt__init__tNoneRtnormalize_tupleRR tnormalize_paddingtpaddingRt input_spec(tselfRR Rtkwargs((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR s  cC@sBtj|d|jd|j|jdƒ}|d||dfS(Niii(Rtconv_output_lengthRRR (Rt input_shapetlength((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pytcompute_output_shapes   cC@s t‚dS(N(tNotImplementedError(RtinputsRR Rt data_format((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyt_pooling_function sc C@s]tj|dƒ}|jd|d|jd d|jd d|jddƒ}tj|dƒS( NiRRiR RRt channels_last(i(i(tKt expand_dimsRRR Rtsqueeze(RRtoutput((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pytcall$s    cC@s_i|jd6|jd6|jd6}tt|ƒjƒ}tt|jƒƒt|jƒƒƒS(NR RR( R RRR Rt get_configtdicttlisttitems(Rtconfigt base_config((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"-s    N( t__name__t __module__t__doc__R R RRR!R"(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR s   t MaxPooling1DcB@s2eZdZejdddd„ƒZd„ZRS(s Max pooling operation for temporal data. # Arguments pool_size: Integer, size of the max pooling windows. strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`. padding: One of `"valid"` or `"same"` (case-insensitive). # Input shape 3D tensor with shape: `(batch_size, steps, features)`. # Output shape 3D tensor with shape: `(batch_size, downsampled_steps, features)`. iRcK@s#tt|ƒj||||dS(N(R R+R (RRR RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR FscC@s%tj|||||ddƒ}|S(Nt pool_modetmax(Rtpool2d(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRLsN(R(R)R*Rtlegacy_pooling1d_supportR R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR+5s tAveragePooling1DcB@s2eZdZejdddd„ƒZd„ZRS(sAverage pooling for temporal data. # Arguments pool_size: Integer, size of the max pooling windows. strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`. padding: One of `"valid"` or `"same"` (case-insensitive). # Input shape 3D tensor with shape: `(batch_size, steps, features)`. # Output shape 3D tensor with shape: `(batch_size, downsampled_steps, features)`. iRcK@s#tt|ƒj||||dS(N(R R0R (RRR RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR dscC@s%tj|||||ddƒ}|S(NR,tavg(RR.(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRjsN(R(R)R*RR/R R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR0Ss t _Pooling2DcB@sGeZdZdd dd d„Zd„Zd„Zd„Zd„ZRS( s4Abstract class for different pooling 2D layers. iRcK@s¤tt|ƒj|tj|ƒ}|dkr:|}ntj|ddƒ|_tj|ddƒ|_tj |ƒ|_ tj|ƒ|_ t ddƒ|_ dS(NiRR R i(R R2R Rtnormalize_data_formatR RRR RRRRR(RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR us  cC@sð|jdkr&|d}|d}n&|jdkrL|d}|d}ntj||jd|j|jdƒ}tj||jd|j|jdƒ}|jdkrÅ|d|d||fS|jdkrì|d|||dfSdS(Ntchannels_firstiiRii(RRRRRR (RRtrowstcols((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRs    cC@s t‚dS(N(R(RRRR RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR‘sc C@s:|jd|d|jd|jd|jd|jƒ}|S(NRRR RR(RRR RR(RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!•s     cC@sii|jd6|jd6|jd6|jd6}tt|ƒjƒ}tt|j ƒƒt|j ƒƒƒS(NRRR R( RRR RR R2R"R#R$R%(RR&R'((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"s     (iiN( R(R)R*R R RRR!R"(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR2qs    t MaxPooling2DcB@s5eZdZejddddd„ƒZd„ZRS(s°Max pooling operation for spatial data. # Arguments pool_size: integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions. strides: Integer, tuple of 2 integers, or None. Strides values. If None, it will default to `pool_size`. padding: One of `"valid"` or `"same"` (case-insensitive). 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, rows, cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, rows, cols)` # Output shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, pooled_rows, pooled_cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, pooled_rows, pooled_cols)` iRcK@s&tt|ƒj|||||dS(N(R R7R (RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR ÏscC@s%tj|||||ddƒ}|S(NR,R-(RR.(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRÕs  (iiN(R(R)R*Rtlegacy_pooling2d_supportR R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR7¦s ' tAveragePooling2DcB@s5eZdZejddddd„ƒZd„ZRS(s´Average pooling operation for spatial data. # Arguments pool_size: integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions. strides: Integer, tuple of 2 integers, or None. Strides values. If None, it will default to `pool_size`. padding: One of `"valid"` or `"same"` (case-insensitive). 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, rows, cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, rows, cols)` # Output shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, pooled_rows, pooled_cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, pooled_rows, pooled_cols)` iRcK@s&tt|ƒj|||||dS(N(R R9R (RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR scC@s%tj|||||ddƒ}|S(NR,R1(RR.(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR s(iiN(R(R)R*RR8R R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR9Ýs ' t _Pooling3DcB@sGeZdZdd dd d„Zd„Zd„Zd„Zd„ZRS( s4Abstract class for different pooling 3D layers. iRcK@s•tt|ƒj||dkr+|}ntj|ddƒ|_tj|ddƒ|_tj|ƒ|_ tj |ƒ|_ t ddƒ|_ dS(NiRR R i(R R:R R RRRR RRR3RRR(RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR s  cC@s3|jdkr0|d}|d}|d}n0|jdkr`|d}|d}|d}ntj||jd|j|jdƒ}tj||jd|j|jdƒ}tj||jd|j|jdƒ}|jdkr|d|d|||fS|jdkr/|d||||dfSdS(NR4iiiRii(RRRRRR (RRtlen_dim1tlen_dim2tlen_dim3((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"s,        cC@s t‚dS(N(R(RRRR RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR:sc C@s:|jd|d|jd|jd|jd|jƒ}|S(NRRR RR(RRR RR(RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!>s     cC@sii|jd6|jd6|jd6|jd6}tt|ƒjƒ}tt|j ƒƒt|j ƒƒƒS(NRRR R( RRR RR R:R"R#R$R%(RR&R'((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"Fs     (iiiN( R(R)R*R R RRR!R"(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR:s    t MaxPooling3DcB@s5eZdZejddddd„ƒZd„ZRS(s’Max pooling operation for 3D data (spatial or spatio-temporal). # Arguments pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension. strides: tuple of 3 integers, or None. Strides values. padding: One of `"valid"` or `"same"` (case-insensitive). 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` # Output shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)` iRcK@s&tt|ƒj|||||dS(N(R R>R (RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR tscC@s%tj|||||ddƒ}|S(NR,R-(Rtpool3d(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRzs(iiiN(R(R)R*Rtlegacy_pooling3d_supportR R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR>Os # tAveragePooling3DcB@s5eZdZejddddd„ƒZd„ZRS(s–Average pooling operation for 3D data (spatial or spatio-temporal). # Arguments pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension. strides: tuple of 3 integers, or None. Strides values. padding: One of `"valid"` or `"same"` (case-insensitive). 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` # Output shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)` iRcK@s&tt|ƒj|||||dS(N(R RAR (RRR RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR ¦scC@s%tj|||||ddƒ}|S(NR,R1(RR?(RRRR RRR ((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR¬s  (iiiN(R(R)R*RR@R R R(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRAs # t_GlobalPooling1DcB@s)eZdZd„Zd„Zd„ZRS(s;Abstract class for different global pooling 1D layers. cK@s,tt|ƒj|tddƒ|_dS(NR i(R RBR RR(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR ¸scC@s|d|dfS(Nii((RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR¼scC@s t‚dS(N(R(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!¿s(R(R)R*R RR!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRB´s  tGlobalAveragePooling1DcB@seZdZd„ZRS(sÝGlobal average pooling operation for temporal data. # Input shape 3D tensor with shape: `(batch_size, steps, features)`. # Output shape 2D tensor with shape: `(batch_size, channels)` cC@stj|ddƒS(Ntaxisi(Rtmean(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!Îs(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRCÃs tGlobalMaxPooling1DcB@seZdZd„ZRS(sÙGlobal max pooling operation for temporal data. # Input shape 3D tensor with shape: `(batch_size, steps, features)`. # Output shape 2D tensor with shape: `(batch_size, channels)` cC@stj|ddƒS(NRDi(RR-(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!Ýs(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRFÒs t_GlobalPooling2DcB@s>eZdZejdd„ƒZd„Zd„Zd„Z RS(s;Abstract class for different global pooling 2D layers. cK@s>tt|ƒj|tj|ƒ|_tddƒ|_dS(NR i(R RGR RR3RRR(RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR åscC@s7|jdkr!|d|dfS|d|dfSdS(NRiii(R(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRëscC@s t‚dS(N(R(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!ñscC@sKi|jd6}tt|ƒjƒ}tt|jƒƒt|jƒƒƒS(NR(RR RGR"R#R$R%(RR&R'((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"ôsN( R(R)R*Rtlegacy_global_pooling_supportR R RR!R"(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRGás   tGlobalAveragePooling2DcB@seZdZd„ZRS(séGlobal average pooling operation for spatial data. # Arguments 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, rows, cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, rows, cols)` # Output shape 2D tensor with shape: `(batch_size, channels)` cC@sE|jdkr(tj|dddgƒStj|dddgƒSdS(NRRDiii(RRRE(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!s(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRIústGlobalMaxPooling2DcB@seZdZd„ZRS(såGlobal max pooling operation for spatial data. # Arguments 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 4D tensor with shape: `(batch_size, rows, cols, channels)` - If `data_format='channels_first'`: 4D tensor with shape: `(batch_size, channels, rows, cols)` # Output shape 2D tensor with shape: `(batch_size, channels)` cC@sE|jdkr(tj|dddgƒStj|dddgƒSdS(NRRDiii(RRR-(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!9s(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRJst_GlobalPooling3DcB@s>eZdZejdd„ƒZd„Zd„Zd„Z RS(s;Abstract class for different global pooling 3D layers. cK@s>tt|ƒj|tj|ƒ|_tddƒ|_dS(NR i(R RKR RR3RRR(RRR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR DscC@s7|jdkr!|d|dfS|d|dfSdS(NRiii(R(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRJscC@s t‚dS(N(R(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!PscC@sKi|jd6}tt|ƒjƒ}tt|jƒƒt|jƒƒƒS(NR(RR RKR"R#R$R%(RR&R'((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR"SsN( R(R)R*RRHR R RR!R"(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRK@s   tGlobalAveragePooling3DcB@seZdZd„ZRS(sVGlobal Average pooling operation for 3D data. # Arguments 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` # Output shape 2D tensor with shape: `(batch_size, channels)` cC@sK|jdkr+tj|ddddgƒStj|ddddgƒSdS(NRRDiiii(RRRE(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!us(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRLYstGlobalMaxPooling3DcB@seZdZd„ZRS(sRGlobal Max pooling operation for 3D data. # Arguments 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". # Input shape - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` # Output shape 2D tensor with shape: `(batch_size, channels)` cC@sK|jdkr+tj|ddddgƒStj|ddddgƒSdS(NRRDiiii(RRR-(RR((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyR!˜s(R(R)R*R!(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pyRM|sN(*t __future__RtRRtengineRRtutilsRtlegacyRRR+R0R2R7R9R:R>RARBRCRFRGRIRJRKRLRMt AvgPool1Dt MaxPool1Dt AvgPool2Dt MaxPool2Dt AvgPool3Dt MaxPool3DtGlobalMaxPool1DtGlobalMaxPool2DtGlobalMaxPool3DtGlobalAvgPool1DtGlobalAvgPool2DtGlobalAvgPool3D(((s3/tmp/pip-build-isqEY4/keras/keras/layers/pooling.pytsF*576<23###%