人工智能与深度学习实战_-_TensorFlow_篇（MD_&_Notebooks）_TensorFlow-Notes.zip

# 模型构建 TensorFlow 中提供了三种方式来构建模型： - 使用 Sequential 按层顺序构建模型 - 使用函数式 API 构建任意结构模型 - 继承 Model 基类构建自定义模型 对于顺序结构的模型，优先使用 Sequential 方法构建。如果模型有多输入或者多输出，或者模型需要共享权重，或者模型具有残差连接等非顺序结构，推荐使用函数式 API 进行创建。如果无特定必要，尽可能避免使用 Model 子类化的方式构建模型，这种方式提供了极大的灵活性，但也有更大的概率出错。 # 案例：IMDB ```py import numpy as np import pandas as pd import tensorflow as tf from tqdm import tqdm from tensorflow.keras import * train_token_path = "./data/imdb/train_token.csv" test_token_path = "./data/imdb/test_token.csv" MAX_WORDS = 10000 # We will only consider the top 10,000 words in the dataset MAX_LEN = 200 # We will cut reviews after 200 words BATCH_SIZE = 20 # 构建管道 def parse_line(line): t = tf.strings.split(line,"\t") label = tf.reshape(tf.cast(tf.strings.to_number(t[0]),tf.int32),(-1,)) features = tf.cast(tf.strings.to_number(tf.strings.split(t[1]," ")),tf.int32) return (features,label) ds_train= tf.data.TextLineDataset(filenames = [train_token_path]) \ .map(parse_line,num_parallel_calls = tf.data.experimental.AUTOTUNE) \ .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE) ds_test= tf.data.TextLineDataset(filenames = [test_token_path]) \ .map(parse_line,num_parallel_calls = tf.data.experimental.AUTOTUNE) \ .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE) ``` ## Sequential 按层顺序创建模型 ```py tf.keras.backend.clear_session() model = models.Sequential() model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN)) model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu")) model.add(layers.MaxPool1D(2)) model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu")) model.add(layers.MaxPool1D(2)) model.add(layers.Flatten()) model.add(layers.Dense(1,activation = "sigmoid")) model.compile(optimizer='Nadam', loss='binary_crossentropy', metrics=['accuracy',"AUC"]) model.summary() ```  ```py import datetime import matplotlib.pyplot as plt baselogger = callbacks.BaseLogger(stateful_metrics=["AUC"]) logdir = "./data/keras_model/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1) history = model.fit(ds_train,validation_data = ds_test, epochs = 6,callbacks=[baselogger,tensorboard_callback]) %matplotlib inline %config InlineBackend.figure_format = 'svg' def plot_metric(history, metric): train_metrics = history.history[metric] val_metrics = history.history['val_'+metric] epochs = range(1, len(train_metrics) + 1) plt.plot(epochs, train_metrics, 'bo--') plt.plot(epochs, val_metrics, 'ro-') plt.title('Training and validation '+ metric) plt.xlabel("Epochs") plt.ylabel(metric) plt.legend(["train_"+metric, 'val_'+metric]) plt.show() plot_metric(history,"AUC") ```  # 函数式 API 创建任意结构模型 ```py tf.keras.backend.clear_session() inputs = layers.Input(shape=[MAX_LEN]) x = layers.Embedding(MAX_WORDS,7)(inputs) branch1 = layers.SeparableConv1D(64,3,activation="relu")(x) branch1 = layers.MaxPool1D(3)(branch1) branch1 = layers.SeparableConv1D(32,3,activation="relu")(branch1) branch1 = layers.GlobalMaxPool1D()(branch1) branch2 = layers.SeparableConv1D(64,5,activation="relu")(x) branch2 = layers.MaxPool1D(5)(branch2) branch2 = layers.SeparableConv1D(32,5,activation="relu")(branch2) branch2 = layers.GlobalMaxPool1D()(branch2) branch3 = layers.SeparableConv1D(64,7,activation="relu")(x) branch3 = layers.MaxPool1D(7)(branch3) branch3 = layers.SeparableConv1D(32,7,activation="relu")(branch3) branch3 = layers.GlobalMaxPool1D()(branch3) concat = layers.Concatenate()([branch1,branch2,branch3]) outputs = layers.Dense(1,activation = "sigmoid")(concat) model = models.Model(inputs = inputs,outputs = outputs) model.compile(optimizer='Nadam', loss='binary_crossentropy', metrics=['accuracy',"AUC"]) model.summary() ``` ```sh Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 200)] 0 __________________________________________________________________________________________________ embedding (Embedding) (None, 200, 7) 70000 input_1[0][0] __________________________________________________________________________________________________ separable_conv1d (SeparableConv (None, 198, 64) 533 embedding[0][0] __________________________________________________________________________________________________ separable_conv1d_2 (SeparableCo (None, 196, 64) 547 embedding[0][0] __________________________________________________________________________________________________ separable_conv1d_4 (SeparableCo (None, 194, 64) 561 embedding[0][0] __________________________________________________________________________________________________ max_pooling1d (MaxPooling1D) (None, 66, 64) 0 separable_conv1d[0][0] __________________________________________________________________________________________________ max_pooling1d_1 (MaxPooling1D) (None, 39, 64) 0 separable_conv1d_2[0][0] __________________________________________________________________________________________________ max_pooling1d_2 (MaxPooling1D) (None, 27, 64) 0 separable_conv1d_4[0][0] __________________________________________________________________________________________________ separable_conv1d_1 (SeparableCo (None, 64, 32) 2272 max_pooling1d[0][0] __________________________________________________________________________________________________ separable_conv1d_3 (SeparableCo (None, 35, 32) 2400 max_pooling1d_1[0][0] __________________________________________________________________________________________________ separable_conv1d_5 (SeparableCo (None, 21, 32) 2528 max_pooling1d_2[0][0] __________________________________________________________________________________________________ global_max_pooling1d (GlobalMax (None, 32) 0 separable_conv1d_1[0][0] __________________________________________________________________________________________________ global_max_pooling1d_1 (GlobalM (None, 32) 0 separable_conv1d_3[0][0] __________________________________________________________________________________________________ global_max_pooling1d_2 (GlobalM (None, 32) 0 separable_conv1d_5[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 96) 0 global_max_pooling1d[0][0] global_max_pooling1d_1[0][0] global_max_pooling1d_2[0][0] __________________________________________________________________________________________________ dense (Dense) (None, 1) 97 concatenate[0][0] ====================================================================================