tf.keras.layers.Dense(int(channel / r))(squeeze)

这行代码是使用 Keras 中的 Dense 层对输入进行全连接操作，输出维度为 channel / r。其中，channel 是输入张量的通道数，r 是一个超参数，用于控制输出维度的缩小比例。这一行代码可以在模型中使用 Squeeze-and-Excitation 模块，用于自适应地调整不同通道的权重。具体来说，它将输入张量进行全局平均池化，然后通过两个全连接层生成一个向量，最后通过 softmax 函数将这个向量转换为权重系数。这些权重系数用于加权输入张量的不同通道，以提高模型的表达能力。

相关问题

# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'

好的，你可以将最佳权重保存路径添加到代码中，如下所示：

import numpy as np
import tensorflow as tf
import os

# 加载数据集
with open('poems.txt', 'r', encoding='utf-8') as f:
    data = f.read()

# 构建词典
vocab = sorted(set(data))
char2idx = {char: idx for idx, char in enumerate(vocab)}
idx2char = np.array(vocab)

# 将文本数据转换为数字
text_as_int = np.array([char2idx[c] for c in data])

# 定义训练数据和标签
seq_length = 100
examples_per_epoch = len(data) // (seq_length + 1)

char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)

sequences = char_dataset.batch(seq_length + 1, drop_remainder=True)

def split_input_target(chunk):
    input_text = chunk[:-1]
    target_text = chunk[1:]
    return input_text, target_text

dataset = sequences.map(split_input_target)

BATCH_SIZE = 128
BUFFER_SIZE = 10000

dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)

# 构建模型
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024

def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(vocab_size, embedding_dim,
                                  batch_input_shape=[batch_size, None]),
        tf.keras.layers.GRU(rnn_units,
                            return_sequences=True,
                            stateful=True,
                            recurrent_initializer='glorot_uniform'),
        tf.keras.layers.Dense(vocab_size)
    ])
    return model

model = build_model(
    vocab_size=len(vocab),
    embedding_dim=embedding_dim,
    rnn_units=rnn_units,
    batch_size=BATCH_SIZE)

# 定义损失函数
def loss(labels, logits):
    return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

# 编译模型
model.compile(optimizer='adam', loss=loss)

# 定义检查点
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")

checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_prefix,
    save_weights_only=True)

# 定义最佳权重检查点
BEST_MODEL_PATH = './best_model.h5'
best_checkpoint = tf.keras.callbacks.ModelCheckpoint(BEST_MODEL_PATH,
                                                      monitor='val_loss',
                                                      save_best_only=True,
                                                      mode='min',
                                                      save_weights_only=True)

# 训练模型
EPOCHS = 50

history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback, best_checkpoint])

# 生成诗歌
def generate_text(model, start_string):
    num_generate = 100
    input_eval = [char2idx[s] for s in start_string]
    input_eval = tf.expand_dims(input_eval, 0)
    text_generated = []
    temperature = 1.0

    model.reset_states()
    for i in range(num_generate):
        predictions = model(input_eval)
        predictions = tf.squeeze(predictions, 0)

        predictions = predictions / temperature
        predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()

        input_eval = tf.expand_dims([predicted_id], 0)

        text_generated.append(idx2char[predicted_id])

    return (start_string + ''.join(text_generated))

# 加载检查点
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)

model.load_weights(BEST_MODEL_PATH)

model.build(tf.TensorShape([1, None]))

# 生成一首诗
print(generate_text(model, start_string=u"山中"))

现在，模型将保存最佳的权重到文件 `best_model.h5`。

SqueezeNet在tensorflow下的python代码实现一个实例

下面是使用TensorFlow实现SqueezeNet的一个简单示例代码：

import tensorflow as tf

def fire_module(x, squeeze=16, expand=64):
    # Squeeze layer
    s = tf.keras.layers.Conv2D(squeeze, (1, 1), activation='relu')(x)
    # Expand layer
    e1 = tf.keras.layers.Conv2D(expand, (1, 1), activation='relu')(s)
    e3 = tf.keras.layers.Conv2D(expand, (3, 3), padding='same', activation='relu')(s)
    # Concatenate expand layers
    output = tf.keras.layers.concatenate([e1, e3], axis=3)
    return output

def SqueezeNet(input_shape=(224, 224, 3), classes=1000):
    input = tf.keras.layers.Input(shape=input_shape)
    
    x = tf.keras.layers.Conv2D(64, (3, 3), strides=(2, 2), padding='valid', activation='relu')(input)
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=16, expand=64)
    x = fire_module(x, squeeze=16, expand=64)
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=32, expand=128)
    x = fire_module(x, squeeze=32, expand=128)
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=64, expand=256)
    x = fire_module(x, squeeze=64, expand=256)
    
    x = tf.keras.layers.Dropout(0.5)(x)
    x = tf.keras.layers.Conv2D(classes, (1, 1), padding='valid')(x)
    x = tf.keras.layers.Flatten()(x)
    output = tf.keras.layers.Dense(classes, activation='softmax')(x)
    
    model = tf.keras.models.Model(input, output, name='SqueezeNet')
    return model

同样，这里也定义了`fire_module`函数，然后通过堆叠多个“Fire模块”和池化层来构建整个SqueezeNet模型。最后，添加一个Dropout层和全连接层来输出分类结果。