Masking [source] ..... 65 5.3 卷积层 Convolutional ..... 66 5.3.1 Conv1D [source] ..... 66 5.3.2 Conv2D [source] ..... 67 5.3.3 SeparableConv2D [source] ..... 68 5.3.4 Conv2DTranspose [source] ... models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD # 生成虚拟数据 x_train = np.random.random((100, 100, 100 -> (100, 100, 3) 张量。 # 使用 32 个大小为 3x3 的卷积滤波器。 model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(Max

(ZeroPadding2D)(None, None, None, 3 0input_1[0][0]conv1_conv (Conv2D)(None, None, None, 6 9472conv1_pad[0][0]conv1_bn None, None, 6 0pool1_pad[0][0]conv2_block1_1_conv (Conv2D)(None, None, None, 6 4160pool1_pool[0][0]conv2_block1_1_bn None, 6 0conv2_block1_1_bn[0][0]conv2_block1_2_conv (Conv2D)(None, None, None, 6 36928conv2_block1_1_relu[0][0]

bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) ## Conv2D It is a convolution 2D layer. It creates a convolutional kernel with the layer input creates a tensor the tuple of integers with RGB value in data_format="channels_last". The signature of the Conv2D function and its arguments with default value is as follows: keras.layers.Conv2D (filters, kernel_size (1, 8, 28) $ values. - First layer, Conv2D consists of 32 filters and 'relu' activation function with kernel size, $ (3,3) $ . - Second layer, Conv2D consists of 64 filters and 'relu'

Framework • New operators like TF quantized_conv2d • Underlying calculations are different than FP32 conv2d real_value = scale * (quantized_value - zero_point) • Sometimes operators are aggressively fused dtype="uint8") / * ty=Tensor[(2, 5), uint8] */; } ## QNN Conv2D Operator ## • Calculations are different from FP32 Conv2D real_value = scale * (quantized_value - zero_point) $$ \Sigma\mathrm{c} s\text\textcircled{z}p_{-}a\times\text\textcircled{z}p_{-}w\quad//Term4\\\end{array} $$ ## Lowering of QNN Conv2D Operator fn (%data: Tensor[(1, 3, 2, 3), uint8], %weight: Tensor[(3, 3, 2, 2), uint8]) { qnn

return num_features net = Net() print(net) Net( (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (fc1): Linear(in_features=400 ) 现在，如果在反向过程中跟随 loss，使用它的 .grad fn 属性，将看到如下所示的计算图。 input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d -> view -> linear -> relu -> linear ->

manufacture a deep learning chip which achieves amazing performance on widely-used operators (e.g. conv2d, dense, ReLU, etc) Now your customer wants to run a YOLO model, but...  can be checked as well def conv2d(attrs, args): return is_float32(args) Return True/False for this op ## After Annotation (e.g., reused parameters) • Multiple outputs (e.g., batch normalization) Subgraph merging (e.g., conv2d + ReLU) ## System Overview ![Image](/uploads/documents/7/a/0/c/7a0c8c1ba296d646f0867cc51cc11426/p12_1

channel dimension in Conv2D C = Reshape((input_shape[1], input_shape[2], 1))(X) # Apply a Conv2D that will transform it into data of dimensions (batchsize, time, 1, NumofFilters) C = Conv2D(filters=init.CNNFilters

CPUCPU total %CPU totalCPU time avg# of Callsaten::conv2d0.11%276.000us93.66%228.600ms4.313ms53 extension: warn(f"Failed to load image Python extension: {e}")

《TensorFlow 快速入门与实战》6-实战TensorFlow验证码识别

name="inputs") # 第1层卷积 conv1 = Conv2D(32, (3, 3), name="conv1")(inputs) relu1 = Activation('relu', name="relu1")(conv1) # 第2层卷积 conv2 = Conv2D(32, (3, 3), name="conv2")(relu1) MaxPooling2D(pool_size=(2, 2), padding='same', name="pool2")(relu2) # 第3层卷积 conv3 = Conv2D(64, (3, 3), name="conv3")(pool2) relu3 = Activation('relu', name="relu3")(conv3)

0 码力 | 51 页 | 2.73 MB | 2 年前

3

aten::conv2d	0.04%	392.0	Stage:2022-12-18 03:59:38 30:30 Activity:Profile 07%								760.0
aten::thnn_conv2d	0.03%	364.0	aten::conv2d	0.07%	398.000us	82.34%	460.282ms	8.685ms	53