文章目录

Mnist模型定义keras层 优化算法及损失函数文字识别模型检验 CNNFilterpoolingexampleimportload data设定神经网络设定优化方法及损失函数训练 RNNLSTM影评分析importload预处理model损失函数&优化方法模型训练打印输出

Mnist

模型定义

tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(hidden_layer_size,activation='rule'), tf.keras.layers.Dropout(0,1), tf.keras.layers.Dense(output_size,activation='softmax') ])

keras层

Dense 全连接层DropoutEmbedding word2vecflatten

优化算法及损失函数

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy', metrics=['accuracy'])

可以使用下面命令看下模型的情况

# Total Params=128*784+128+(128*10+10)=101,770 model.summary()

文字识别

# 总的训练次数,每次训练全量数据,然后梯度下降调整参数 NUM_EPOCHS=10 # 数据归一化 x_train,x_valid,x_test=x_train/255.0,x_valid/255.0,x_test/255.0 # verbose控制训练过程中显示参数 history=model.fit(x_train,y_train,epochs=NUM_EPOCHS, validation_data=(x_valid,y_valid),verbose=2)

模型检验

test_loss,test_accuracy=model.evaluate(x_test,y_test)

CNN

Filter

每个卷积核会提取图片某种特征,相当于滤镜 下面的卷积会锐化图片

0-10-15-10-10

下面的卷积会检查出边缘

0101-41010

pooling

池化操作主要是为了降维,常见操作是提取最大值

1124567832101234

按2*2卷积后

6834

example

import

import numpy as np import tersorflow as tf import pandas as pd from tensorflow import keras import matplotlib as mpl import matplotlib.pyplot as plt %matplotlib inline

load data

npz=np.load(...) x_train=npz['inputs'].astype(np.float) y_train=npz['targets'].astype(np.int) npz=np.load(...) x_valid,y_valid=npz['inputs'].astype(np.float).npz('targets').astype(np.int) # x_test,y_test # 图片其实是三维数据(颜色),所以下面需要带上0 x_train[0].shape from sklearn.preprocessing import StandardScaler scaler=StandardScaler() x_train_scaled=scaler.fit_transform(x_tratin.astype(np.float32).reshape(-1,1)) .reshape(-1,28,28,1) # x_valid_scaled x_test_scaled

设定神经网络

model=keras.models.Sequential() model.add(keras.layer.Conv2D(filters=32,padding='same',kernel_size=3, activation='selu',input_shape=(28,28,1))) model.add(keras.layers.MaxPool2D(poo_size=) model.add(keras.layers.Flatten()) model.add(keras.layer.Dense(128,activat='selu')) model.add(keras.layer.Dense(10,activation='softmax'))

设定优化方法及损失函数

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy']) # 可以使用下列方法看下模型 model.summary() Model:"sequential" --------------------------------------------------------------------------- Layer(type) Output Shape Param # =========================================================================== conv2d(Conv2D) (None,28,28,32) 320 max_pooling2d(MaxPooling2D) (None,14,14,32) 0 flatten(Flatten) (None,6272) 0 dense(Dense) (None,128) 802944 dense_1(Dense) (None,10) 1290 =========================================================================== Total params:804,554 Trainable params:804,554 Non-trainable params:0 ---------------------------------------------------------------------------

320=32*((3*3)+1) 6272=14*14*32

训练

import os # 训练日志存储目录 logdir='cnn-selu-callbacks' if not os.path.exists(logdir): os.mkdir(logdir) output_model_file=os.path.join(logdir,'model.h5') callbacks=[ #训练完成后CML运行tensorboard --logdir=cnn-selu-callbacks启动tensorboard服务(6006端口) keras.callbacks.TensorBoard(logdir), keras.callbacks.ModelCheckpoint(output_model_file,save_best_only=True), keras.callbacks.EarlyStopping(patience=5,min_delta=le-3), ] history=model.fit(x_train_scaled,y_train,epochs=10, validation_data=(x_valid_scaled,y_valid),callbacks=callbacks)

RNN

$$h_t=f_W(h_{t-1},x_t)$$

LSTM

影评分析

import

import matplotlib as mpl import matplotlib.pyplot as plt %matplotlib inline import numpy as np import sklearn import pandas as pd import os import sys import time import tensorflow tf from tensorflow import keras

load

npz=np.load(r'data\indb_data_train.npz') x_train=npz['inputs'] y_train=npz['targets'] #x_test y_test

预处理

imdb=keras.datasets.imdb word_index=imdb.get_word_index() # print(len(word_index)) word_index={k:(v+3) for k,v in word_index.items()} word_index['<PAD>']=0 word_index['<START>']=1 word_index['<UNK>']=2 word_index['<END>']=3 reverse_word_index=dict([(value,key) for key,value in word_index.items()])

进入神经网络的每段文字应当具有相同长度,过长的文字需要截断,过短的问题需要填充空白字符

max_length=500 x_train=keras.preprocession.sequence.pad_sequences(x_train, value=word_index['<PAD>'],padding='post',maxlen=max_length) #x_test

model

embedding层生成词向量Word2Vec,embedding_dim为每个单词的词向量维度

embedding_dim=16 batch_size=512 vocab_size=10000 rnn_model=keras.models.Sequential([ keras.layers.Embedding(input_dim=vocab_size,output_dim=embedding_dim,input_length=max_length), keras.layers.Bidirectional(keras.layers.SimpleRNN(units=32,return_sequences=True)), keras.layers.Bidirectional(keras.layers.SimpleRNN(units=32,return_sequences=False)), keras.layers.Dense(64,activation='relu'), keras.layers.Dense(1,activation='sigmoid'), ]) #rnn_model.summary()

其中return_sequences用于标识下一层是否还是RNN

损失函数&优化方法

rnn_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

模型训练

history=rnn_model.fit(x_train,y_train,epochs=10, batch_size=batch_size,validation_split=0.2)

打印输出

def plot_learning_curves(history,label,epochs,min_value,max_value): data={} data[label]=history.history[label] data['val_'+label]=history.history['val_'+label] pd.DataFrame(data).plot(figsize=(8,5)) plt.grid(True) plt.axis([0,epochs,min_value,max_value]) plt.show() plot_learning_curves(history,'accuracy',10,0,1) plot_learning_curves(history,'loss',10,0,1)