def mymodel(): inputs = keras.Input(shape=(x_train.shape[1], x_train.shape[2])) h1 = layers.Conv1D(filters=8, kernel_size=3, strides=1, padding='same', activation='relu')(inputs) h1 = layers.MaxPool1D(pool_size=2, strides=2, padding='same')(h1) h1 = layers.Conv1D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu')(h1) h1 = layers.MaxPool1D(pool_size=2, strides=2, padding='same')(h1) h1 = layers.Flatten()(h1) h1 = layers.Dropout(0.6)(h1) h1 = layers.Dense(32, activation='relu')(h1) h1 = layers.Dense(10, activation='softmax')(h1) deep_model = keras.Model(inputs, h1, name="cnn") return deep_model model = mymodel() model.summary()#把整个网络打印出来 startdate = datetime.utcnow() # 获取当前时间 # 编译模型 model.compile( optimizer=keras.optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, y_train, batch_size=512, epochs=100, verbose=1, validation_data=(x_valid, y_valid), callbacks=[CustomModelCheckpoint( model, r'best_fft_1dcnn_512.h5')])定义的模型训练好后怎么加载使用

训练好的模型可以通过`load_model()`函数加载并使用。你可以按照以下步骤加载和使用模型：

1. 首先，导入`keras.models`模块：

   from keras.models import load_model

2. 使用`load_model()`函数加载模型，并将其保存在变量中：

   loaded_model = load_model('best_fft_1dcnn_512.h5')

3. 现在，你可以使用`loaded_model`变量来对新数据进行预测或评估：

   predictions = loaded_model.predict(new_data)

请注意，加载的模型将包含之前训练时保存的权重和模型架构。确保在加载模型时，模型架构与之前训练时的架构完全一致。

希望这个回答对你有帮助！如果你有任何其他问题，请随时提问。

相关问题

# 设置输入层节点数、隐层节点数 in_nodes=784 h1_nodes=100 h2_nodes=100 h3_nodes=50 # 定义输入、输出、prob的placeholder x=tf.placeholder(tf.float32,[None,in_nodes]) y_=tf.placeholder(tf.float32,[None,10]) prob=tf.placeholder(tf.float32) # 设置第一隐层 w1=weight(in_nodes, h1_nodes, 0.1, 0.005) b1=tf.Variable(tf.zeros([h1_nodes])) h1=tf.nn.relu(tf.matmul(x,w1)+b1) # 设置第二隐层 w2=weight(h1_nodes, h2_nodes, 0.1, 0.0) b2=tf.Variable(tf.zeros([h2_nodes])) h2=tf.nn.relu(tf.matmul(h1,w2)+b2) h2_drop=tf.nn.dropout(h2, prob) # 设置第三隐层 w3=weight(h2_nodes, h3_nodes, 0.1, 0.0) b3=tf.Variable(tf.zeros([h3_nodes])) h3=tf.nn.relu(tf.matmul(h2_drop,w3)+b3) h3_drop=tf.nn.dropout(h3, prob) # 设置softmax输出层 w4=weight(h3_nodes, 10, 0.1, 0.0) b4=tf.Variable(tf.zeros([10])) y=tf.nn.softmax(tf.matmul(h3_drop,w4)+b4)

将上述代码改写成 TensorFlow 2.x 版本的代码如下：

import tensorflow as tf

def weight(shape, stddev, wd):
    initial = tf.random.truncated_normal(shape, stddev=stddev)
    var = tf.Variable(initial)
    if wd is not None:
        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
        tf.add_to_collection('losses', weight_decay)
    return var

class MyModel(tf.keras.Model):
    def __init__(self, in_nodes=784, h1_nodes=100, h2_nodes=100, h3_nodes=50):
        super(MyModel, self).__init__()
        
        self.w1 = weight([in_nodes, h1_nodes], 0.1, 0.005)
        self.b1 = tf.Variable(tf.zeros([h1_nodes]))
        self.w2 = weight([h1_nodes, h2_nodes], 0.1, 0.0)
        self.b2 = tf.Variable(tf.zeros([h2_nodes]))
        self.w3 = weight([h2_nodes, h3_nodes], 0.1, 0.0)
        self.b3 = tf.Variable(tf.zeros([h3_nodes]))
        self.w4 = weight([h3_nodes, 10], 0.1, 0.0)
        self.b4 = tf.Variable(tf.zeros([10]))
        
    def call(self, inputs, prob):
        x = inputs
        y_ = tf.cast(inputs, tf.float32)
        h1 = tf.nn.relu(tf.matmul(x, self.w1) + self.b1)
        h2 = tf.nn.relu(tf.matmul(h1, self.w2) + self.b2)
        h2_drop = tf.nn.dropout(h2, rate=prob)
        h3 = tf.nn.relu(tf.matmul(h2_drop, self.w3) + self.b3)
        h3_drop = tf.nn.dropout(h3, rate=prob)
        y = tf.nn.softmax(tf.matmul(h3_drop, self.w4) + self.b4)
        return y

model = MyModel()
x = tf.keras.Input(shape=(None, 784))
prob = tf.keras.Input(shape=())
y = model(x, prob)
y_ = tf.keras.Input(shape=(None, 10))

# 定义损失函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(y), reduction_indices=[1]))
tf.add_to_collection('losses', cross_entropy)
loss = tf.add_n(tf.get_collection('losses'))

# 定义优化器
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys, prob: 0.5})

在 TensorFlow 2.x 中，可以使用 `tf.reduce_mean` 和 `tf.reduce_sum` 函数来计算张量的平均值和总和；使用 `tf.math.log` 函数来计算张量的自然对数。此外，可以使用 `tf.train.AdamOptimizer` 来定义优化器，使用 `model.trainable_variables` 来获取所有可训练的变量。