摘要：全连接卷积可训练参数个数 = 上层输出神经元个数（输入神经元个数）* 卷积核面积 * 本层卷积输出神经元个数 + 本层卷积输出神经元个数或全连接卷积可训练参数个数 =（上层输出神经元个数（输入神经元个数）* 卷积核面积 + 1）* 本层卷积输出神经元个数 阅读全文

摘要：关于输入多张样本图片一次卷积输出的总参数个数--总连接数以及参数个数与连接个数的关系推导公式 多张样本图输入一次卷积输出的参数个数=（输入样本特征图张数 * 卷积核面积 + 1个偏置）* 输出的特征图总张数 多张样本图输入一次卷积输出的总连接数=（输入样本特征图张数 * 卷积核面积 + 1个偏置）* 阅读全文

摘要：# 1. 自定义模型并训练。 import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) def lenet(x, is_training): x = tf.reshape 阅读全文

摘要：# 1. 模型定义。 import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) mnist = input_data.read_data_sets("F:\\Tensor 阅读全文

摘要：# 1. 模型定义。 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist_data = input_data.read_data_sets('F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data', one_... 阅读全文

摘要：# 1. 数据预处理。 import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_ro... 阅读全文

摘要：# 1. 数据预处理。 import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_ro... 阅读全文

摘要：# 1. 数据预处理。 from keras.layers import LSTM from keras.datasets import imdb from keras.models import Sequential from keras.preprocessing import sequence from keras.layers import Dense, Embedding max_fea 阅读全文

摘要：# 1. 数据预处理 import keras from keras import backend as K from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D num_classes = 阅读全文

摘要：import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platform import gfile import tensorflow.contrib.slim as slim # 加载通过TensorFlow-Slim定义好的inception_v3模型。 impor 阅读全文

摘要：import tensorflow as tf INPUT_NODE = 784 OUTPUT_NODE = 10 IMAGE_SIZE = 28 NUM_CHANNELS = 1 NUM_LABELS = 10 CONV1_DEEP = 32 CONV1_SIZE = 5 CONV2_DEEP = 64 CONV2_SIZE = 5 FC_SIZE = 512 def inference(inp 阅读全文

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摘要：import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 # 输入节点 OUTPUT_NODE = 10 # 输出节点 LAYER1_NODE = 500 # 隐藏层数 BATCH_SIZE = 100 # 每次batch打包的样本个数 # 模型相关的参数 阅读全文

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摘要：http://playground.tensorflow.org/ 阅读全文

摘要：import tensorflow as tf a = tf.constant([1.0, 2.0], name="a") b = tf.constant([2.0, 3.0], name="b") result = a + b print(result) import... 阅读全文

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摘要：import os import numpy as np import pandas as pd from datetime import datetime import matplotlib import matplotlib.pyplot as plt import seaborn as sns sns.set_style('white') %matplotlib inline %lo... 阅读全文

摘要：import pandas as pd pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]}) pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']}) pd.DataFrame({'Bob': ['I like... 阅读全文

摘要：dfS = read.csv("F:\\kaggleDataSet\\spacex-missions\\database.csv") library(dplyr) library(tidyr) library(data.table) library(sqldf) library(highcharter) library(ggrepel) library(leaflet) library(v... 阅读全文