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本文整理汇总了Python中keras.layers.ZeroPadding3D方法的典型用法代码示例。如果您正苦于以下问题：Python layers.ZeroPadding3D方法的具体用法？Python layers.ZeroPadding3D怎么用？Python layers.ZeroPadding3D使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.ZeroPadding3D方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: test_keras_import# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def test_keras_import(self): # Pad 1D model = Sequential() model.add(ZeroPadding1D(2, input_shape=(224, 3))) model.add(Conv1D(32, 7, strides=2)) model.build() self.pad_test(model, 'pad_w', 2) # Pad 2D model = Sequential() model.add(ZeroPadding2D(2, input_shape=(224, 224, 3))) model.add(Conv2D(32, 7, strides=2)) model.build() self.pad_test(model, 'pad_w', 2) # Pad 3D model = Sequential() model.add(ZeroPadding3D(2, input_shape=(224, 224, 224, 3))) model.add(Conv3D(32, 7, strides=2)) model.build() self.pad_test(model, 'pad_w', 2)# ********** Export json tests **********# ********** Data Layers Test **********
示例2: pad_to_multiple# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def pad_to_multiple(x, block_shape): ori_shape = K.int_shape(x) pad_t = ori_shape[1] % block_shape[0] pad_f = ori_shape[2] % block_shape[1] padding=((0, 0), (0, pad_t), (0, pad_f)) new_x = L.ZeroPadding3D(padding=padding)(x) return new_x
开发者ID:BreezeWhite，项目名称:Music-Transcription-with-Semantic-Segmentation，代码行数:12，代码来源:attn_utils.py
示例3: c3d# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def c3d(self): """ Build a 3D convolutional network, aka C3D. https://arxiv.org/pdf/1412.0767.pdf With thanks: https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2 """ model = Sequential() # 1st layer group model.add(Conv3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1), input_shape=self.input_shape)) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')) # 2nd layer group model.add(Conv3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')) # 3rd layer group model.add(Conv3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))) model.add(Conv3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')) # 4th layer group model.add(Conv3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))) model.add(Conv3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1))) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')) # 5th layer group model.add(Conv3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv5a', subsample=(1, 1, 1))) model.add(Conv3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv5b', subsample=(1, 1, 1))) model.add(ZeroPadding3D(padding=(0, 1, 1))) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool5')) model.add(Flatten()) # FC layers group model.add(Dense(4096, activation='relu', name='fc6')) model.add(Dropout(0.5)) model.add(Dense(4096, activation='relu', name='fc7')) model.add(Dropout(0.5)) model.add(Dense(self.nb_classes, activation='softmax')) return model
开发者ID:harvitronix，项目名称:five-video-classification-methods，代码行数:63，代码来源:models.py
示例4: multihead_attention# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def multihead_attention(x, out_channel=64, d_model=32, n_heads=8, query_shape=(128, 24), memory_flange=(8, 8)): q = Conv2D(d_model, (3, 3), strides=(1, 1), padding="same", name="gen_q_conv")(x) k = Conv2D(d_model, (3, 3), strides=(1, 1), padding="same", name="gen_k_conv")(x) v = Conv2D(d_model, (3, 3), strides=(1, 1), padding="same", name="gen_v_conv")(x) q = split_heads_2d(q, n_heads) k = split_heads_2d(k, n_heads) v = split_heads_2d(v, n_heads) k_depth_per_head = d_model // n_heads q *= k_depth_per_head**-0.5 """ # local attetion 2d v_shape = K.int_shape(v) q = pad_to_multiple(q, query_shape) k = pad_to_multiple(k, query_shape) v = pad_to_multiple(v, query_shape) paddings = ((0, 0), (memory_flange[0], memory_flange[1]), (memory_flange[0], memory_flange[1])) k = L.ZeroPadding3D(padding=paddings)(k) v = L.ZeroPadding3D(padding=paddings)(v) # Set up query blocks q_indices = gather_indices_2d(q, query_shape, query_shape) q_new = gather_blocks_2d(q, q_indices) # Set up key and value blocks memory_shape = (query_shape[0] + 2*memory_flange[0], query_shape[1] + 2*memory_flange[1]) k_and_v_indices = gather_indices_2d(k, memory_shape, query_shape) k_new = gather_blocks_2d(k, k_and_v_indices) v_new = gather_blocks_2d(v, k_and_v_indices) output = dot_attention(q_new, k_new, v_new) # Put output back into original shapes padded_shape = K.shape(q) output = scatter_blocks_2d(output, q_indices, padded_shape) # Remove padding output = K.slice(output, [0, 0, 0, 0, 0], [-1, -1, v_shape[2], v_shape[3], -1]) """ output = local_attention_2d(q, k, v, query_shape=query_shape, memory_flange=memory_flange) output = combine_heads_2d(output) output = Conv2D(out_channel, (3, 3), strides=(1, 1), padding="same", use_bias=False)(output) return output
开发者ID:BreezeWhite，项目名称:Music-Transcription-with-Semantic-Segmentation，代码行数:53，代码来源:model_attn.py
示例5: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def build(video_shape, audio_spectrogram_size): model = Sequential() model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero1', input_shape=video_shape)) model.add(Convolution3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero2')) model.add(Convolution3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 1, 1), name='zero3')) model.add(Convolution3D(128, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3')) model.add(Dropout(0.25)) model.add(TimeDistributed(Flatten(), name='time')) model.add(Dense(1024, kernel_initializer='he_normal', name='dense1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(1024, kernel_initializer='he_normal', name='dense2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2048, kernel_initializer='he_normal', name='dense3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(2048, kernel_initializer='he_normal', name='dense4')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(audio_spectrogram_size, name='output')) model.summary() return VideoToSpeechNet(model)
开发者ID:avivga，项目名称:cocktail-party，代码行数:55，代码来源:network.py
示例6: pooling# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ZeroPadding3D [as 别名]def pooling(layer, layer_in, layerId, tensor=True): poolMap = { ('1D', 'MAX'): MaxPooling1D, ('2D', 'MAX'): MaxPooling2D, ('3D', 'MAX'): MaxPooling3D, ('1D', 'AVE'): AveragePooling1D, ('2D', 'AVE'): AveragePooling2D, ('3D', 'AVE'): AveragePooling3D, } out = {} layer_type = layer['params']['layer_type'] pool_type = layer['params']['pool'] padding = get_padding(layer) if (layer_type == '1D'): strides = layer['params']['stride_w'] kernel = layer['params']['kernel_w'] if (padding == 'custom'): p_w = layer['params']['pad_w'] out[layerId + 'Pad'] = ZeroPadding1D(padding=p_w)(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] elif (layer_type == '2D'): strides = (layer['params']['stride_h'], layer['params']['stride_w']) kernel = (layer['params']['kernel_h'], layer['params']['kernel_w']) if (padding == 'custom'): p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w'] out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] else: strides = (layer['params']['stride_h'], layer['params']['stride_w'], layer['params']['stride_d']) kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'], layer['params']['kernel_d']) if (padding == 'custom'): p_h, p_w, p_d = layer['params']['pad_h'], layer['params']['pad_w'],/ layer['params']['pad_d'] out[layerId + 'Pad'] = ZeroPadding3D(padding=(p_h, p_w, p_d))(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] # Note - figure out a permanent fix for padding calculation of layers # in case padding is given in layer attributes # if ('padding' in layer['params']): # padding = layer['params']['padding'] out[layerId] = poolMap[(layer_type, pool_type)]( pool_size=kernel, strides=strides, padding=padding) if tensor: out[layerId] = out[layerId](*layer_in) return out# ********** Locally-connected Layers **********
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