Json Lee lijiansong

## checkmate_tutorial_basic_tf2_example.ipynb

      
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                lijiansong
                / checkmate_tutorial_basic_tf2_example.ipynb
            
            
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              May 8, 2021 01:18
            
              
                tutorial_basic_tf2_example.ipynb
              
          
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## gpu_moive_review.py
import math
import datetime

from tqdm import tqdm

import pandas as pd
import numpy as np

import tensorflow as tf

## autoencoder-full.py
"""TensorFlow 2.0 implementation of vanilla Autoencoder."""
import numpy as np
import tensorflow as tf

__author__ = "Abien Fred Agarap"

np.random.seed(1)
tf.random.set_seed(1)
batch_size = 128
epochs = 10

## tf-2.0-build-from-source.md

      
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                lijiansong
                / tf-2.0-build-from-source.md
            
            
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              March 25, 2021 02:48
            
          
    Build TF 2.0 from source

Step 1:
从anaconda的base环境中创建新的环境：
$ conda create -n tf2-source python=3.6
$ conda activate tf2-source


## faster-rcnn.prototxt
name: "ZF"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 720 #770 725
  dim: 1280 #683 1285
}

input: "im_info"

## mobilenet-v2.prototxt
name: "MOBILENET_V2"
#  transform_param {
#    scale: 0.017
#    mirror: false
#    crop_size: 224
#    mean_value: [103.94,116.78,123.68]
#  }
input: "data"
input_shape {
  dim: 1

## ssd-vgg16.prototxt
name: "VGG_VOC0712_SSD_300x300_deploy"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 300
  dim: 300
}

layer {

## ssd-mobilenetv1.prototxt
name: "MobileNet-SSD"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 300
  dim: 300
}

layer {

## densenet121.protxt
name: "DENSENET_121"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 224
  dim: 224
}
layer {
  name: "conv1"

## resnet50.prototxt
name: "ResNet-50"
input: "data"
input_shape{
  dim: 1
  dim: 3
  dim: 224
  dim: 224
}

layer {
	import math
	import datetime

	from tqdm import tqdm

	import pandas as pd
	import numpy as np

	import tensorflow as tf
	"""TensorFlow 2.0 implementation of vanilla Autoencoder."""
	import numpy as np
	import tensorflow as tf

	__author__ = "Abien Fred Agarap"

	np.random.seed(1)
	tf.random.set_seed(1)
	batch_size = 128
	epochs = 10
	name: "ZF"
	input: "data"
	input_shape {
	dim: 1
	dim: 3
	dim: 720 #770 725
	dim: 1280 #683 1285
	}

	input: "im_info"
	name: "MOBILENET_V2"
	# transform_param {
	# scale: 0.017
	# mirror: false
	# crop_size: 224
	# mean_value: [103.94,116.78,123.68]
	# }
	input: "data"
	input_shape {
	dim: 1
	name: "VGG_VOC0712_SSD_300x300_deploy"
	input: "data"
	input_shape {
	dim: 1
	dim: 3
	dim: 300
	dim: 300
	}

	layer {
	name: "MobileNet-SSD"
	input: "data"
	input_shape {
	dim: 1
	dim: 3
	dim: 300
	dim: 300
	}

	layer {
	name: "DENSENET_121"
	input: "data"
	input_shape {
	dim: 1
	dim: 3
	dim: 224
	dim: 224
	}
	layer {
	name: "conv1"
	name: "ResNet-50"
	input: "data"
	input_shape{
	dim: 1
	dim: 3
	dim: 224
	dim: 224
	}

	layer {