TensorFlow 2.0 - CNN / 预训练 / RNN

文章目录

- 1. CNN 卷积神经网络
- 2. 预训练模型
- 3. RNN 循环神经网络

学习于：简单粗暴 TensorFlow 2

1. CNN 卷积神经网络

卷积神经网络，卷积后尺寸计算

tf.keras.layers.Conv2D， tf.keras.layers.MaxPool2D

# CNN 模型
class myCNN(tf.keras.Model):def __init__(self):super().__init__()self.conv1 = tf.keras.layers.Conv2D(filters=32,kernel_size=[5,5],padding='same',activation='relu')self.pool1 = tf.keras.layers.MaxPool2D(pool_size=[2,2],strides=2)self.conv2 = tf.keras.layers.Conv2D(filters=64,kernel_size=[5,5],padding='same',activation='relu')self.pool2 = tf.keras.layers.MaxPool2D(pool_size=[2,2],strides=2)self.flatten = tf.keras.layers.Reshape(target_shape=(7*7*64,))self.dense1 = tf.keras.layers.Dense(units=1024,activation='relu')self.dense2 = tf.keras.layers.Dense(units=10)def call(self, inputs): # [m, 28 , 28 , 1]# size = (n+2p-f)/s + 1，same 保持尺寸不变x = self.conv1(inputs) # [m, 28 , 28 , 32]# 池化时 p 常为 0x = self.pool1(x) # [m, 14 , 14 , 32]x = self.conv2(x) # [m, 14 , 14 , 64]x = self.pool2(x) # [m, 7 , 7 , 64]x = self.flatten(x) # [m, 7*7*64]x = self.dense1(x) # [m, 1024]x = self.dense2(x) # [m, 10]outputs = tf.nn.softmax(x) # [m, 10]return outputs

2. 预训练模型

mymodel = tf.keras.applications.MobileNetV2()，可以调用 VGG16 、 VGG19 、 ResNet 、 MobileNet 等内置模型，使用预训练好的权重初始化网络

import tensorflow as tf
import tensorflow_datasets as tfdsnum_epoch = 2
batch_size = 16
learning_rate = 1e-3version = tf.__version__
gpu_ok = tf.config.list_physical_devices('GPU')
print("tf version:", version, "\nuse GPU", gpu_ok)# 会自动加载数据集，从网络下载
dataset = tfds.load("tf_flowers", split=tfds.Split.TRAIN, as_supervised=True)
# 数据归一化、打乱、分批
dataset = dataset.map(lambda img, label: (tf.image.resize(img, (224, 224)) / 255.0, label)).shuffle(1024).batch(batch_size)# 加载预训练模型
model = tf.keras.applications.MobileNetV2(include_top=True, weights=None, classes=5)
# weights 默认： "imagenet"， 也可以不用预训练权值，include_top 有最后的FC层
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
for e in range(num_epoch):for images, labels in dataset:with tf.GradientTape() as tape:pred = model(images, training=True) # *****设置training模式*****loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=labels, y_pred=pred)loss = tf.reduce_mean(loss)print("loss: {}".format(loss.numpy()))grads = tape.gradient(loss, model.trainable_variables)optimizer.apply_gradients(grads_and_vars=zip(grads, model.trainable_variables))

注：如果数据集下载缓慢，我传到csdn了，免费下载

3. RNN 循环神经网络

数据预处理，字符与 idx 的相互转换映射，字符集
获取 batch_size 个样本、每个样本的下一个字符（标签）

import tensorflow as tf
import numpy as npclass Dataloader():def __init__(self):path = tf.keras.utils.get_file('nietzsche.txt',origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')with open(path, encoding='utf-8') as f:self.raw_text = f.read().lower()self.chars = sorted(list(set(self.raw_text)))self.char_idx = dict((c, i) for i, c in enumerate(self.chars))self.idx_char = dict((i, c) for i, c in enumerate(self.chars))self.text = [self.char_idx[c] for c in self.raw_text]  # 原始文本的 idsdef get_batch(self, seq_len, batch_size):seq = []next_char = []for i in range(batch_size):idx = np.random.randint(0, len(self.text) - seq_len)seq.append(self.text[idx: idx + seq_len])next_char.append(self.text[idx + seq_len])return np.array(seq), np.array(next_char)  # [batch_size, seq_len] [batch_size]

建模，tf.keras.layers.LSTMCell

class myRNN(tf.keras.Model):def __init__(self, num_chars, batch_size, seq_len):super().__init__()self.num_chars = num_charsself.seq_len = seq_lenself.batch_size = batch_sizeself.cell = tf.keras.layers.LSTMCell(units=256)self.dense = tf.keras.layers.Dense(units=self.num_chars)def call(self, inputs, from_logits=False):inputs = tf.one_hot(inputs, depth=self.num_chars)  # [batch_size, seq_len, num_chars]# 获取初始状态state = self.cell.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)  # [batch_size, 256]for t in range(self.seq_len):output, state = self.cell(inputs[:, t, :], state)logits = self.dense(output)  # [batch_size, num_chars]if from_logits: # 控制是否softmax归一化return logitselse:return tf.nn.softmax(logits)def predict(self, inputs, temperature=1.0): # temperature调节词汇的丰富度，对概率进行调整batch_size, _ = tf.shape(inputs)logits = self(inputs, from_logits=True)  # self 调用 __call()__, __call()__ 调用 call()prob = tf.nn.softmax(logits / temperature).numpy()  # [batch_size 64, num_chars]return np.array([np.random.choice(self.num_chars, p=prob[i, :])for i in range(batch_size.numpy())])  # 每个样本，下一个字符 按预测概率随机采样

训练

num_batches = 1000
seq_len = 40 # 输入序列长度
batch_size = 64 
learning_rate = 1e-3data_loader = Dataloader()
model = myRNN(num_chars=len(data_loader.chars),batch_size=batch_size,seq_len=seq_len)# 优化器              
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)# 训练迭代
for i in range(num_batches):# 获取批量数据X, y = data_loader.get_batch(seq_len, batch_size)# 梯度记录器with tf.GradientTape() as tape:# 前向传播y_pred = model(X)# 计算损失loss = tf.keras.losses.sparse_categorical_crossentropy(y, y_pred)loss = tf.reduce_mean(loss)print("batch：{}, loss {}".format(i, loss.numpy()))# 计算梯度grads = tape.gradient(loss, model.variables)# 更新参数optimizer.apply_gradients(zip(grads, model.variables))

预测

# 拿出一个样本
X_, _ = data_loader.get_batch(seq_len, 1)
for diversity in [0.2, 0.5, 1.0, 1.2]: # 词汇丰富度X = X_print('diversity {}'.format(diversity))for t in range(400): # 输出长度 400 y_pred = model.predict(X, diversity) # 预测字符的 idprint(data_loader.idx_char[y_pred[0]], end='', flush=True) # 输出预测的字符X = np.concatenate([X[:, 1:], np.expand_dims(y_pred, axis=1)], axis=-1) # 滑动1位，作为下一次的输入print("\n")

输出：

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