deep_flatten

`deep_flatten`这个词组通常指的是深度扁平化，这是一个在数据科学和机器学习中常见的概念，特别是在处理深度学习模型的输出或多层次的数据结构（如张量、列表嵌套等）时。它的目标是将一个多维数组（例如嵌套列表或卷积神经网络的输出）转换成一维数组，便于进一步的分析、计算或者作为模型输入。

深度扁平化函数可以递归地遍历多层嵌套结构，逐层展开，将所有元素合并到单一的一维序列中。这种操作有助于简化后续的数据处理流程，比如批量处理、向量化运算等。

举个例子，如果你有一个形状为(5,2,3)的三维数组，`deep_flatten`函数将返回一个长度为30的一维数组（因为5*2*3=30）。

相关问题

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropoutfrom tensorflow.keras import Model​# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果tf.random.set_seed(100)​mnist = tf.keras.datasets.mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train, X_test = X_train/255.0, X_test/255.0​# 将特征数据集从(N,32,32)转变成(N,32,32,1),因为Conv2D需要(NHWC)四阶张量结构X_train = X_train[..., tf.newaxis] X_test = X_test[..., tf.newaxis]​batch_size = 64# 手动生成mini_batch数据集train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)​class Deep_CNN_Model(Model): def __init__(self): super(Deep_CNN_Model, self).__init__() self.conv1 = Conv2D(32, 5, activation='relu') self.pool1 = MaxPool2D() self.conv2 = Conv2D(64, 5, activation='relu') self.pool2 = MaxPool2D() self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.dropout = Dropout(0.2) self.d2 = Dense(10, activation='softmax') def call(self, X): # 无需在此处增加training参数状态。只需要在调用Model.call时，传递training参数即可 X = self.conv1(X) X = self.pool1(X) X = self.conv2(X) X = self.pool2(X) X = self.flatten(X) X = self.d1(X) X = self.dropout(X) # 无需在此处设置training状态。只需要在调用Model.call时，传递training参数即可 return self.d2(X)​model = Deep_CNN_Model()loss_object = tf.keras.losses.SparseCategoricalCrossentropy()optimizer = tf.keras.optimizers.Adam()​train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')​# TODO：定义单批次的训练和预测操作@tf.functiondef train_step(images, labels): ...... @tf.functiondef test_step(images, labels): ...... # TODO：执行完整的训练过程EPOCHS = 10for epoch in range(EPOCHS)补全代码

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
from tensorflow.keras import Model

# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果
tf.random.set_seed(100)

# 加载MNIST数据集并进行预处理
mnist = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train/255.0, X_test/255.0
X_train = X_train[..., tf.newaxis]
X_test = X_test[..., tf.newaxis]

# 定义batch_size并手动生成mini_batch数据集
batch_size = 64
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)

# 定义深度CNN模型
class Deep_CNN_Model(Model):
def __init__(self):
super(Deep_CNN_Model, self).__init__()
self.conv1 = Conv2D(32, 5, activation='relu')
self.pool1 = MaxPool2D()
self.conv2 = Conv2D(64, 5, activation='relu')
self.pool2 = MaxPool2D()
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.dropout = Dropout(0.2)
self.d2 = Dense(10, activation='softmax')

def call(self, X, training=False):
X = self.conv1(X)
X = self.pool1(X)
X = self.conv2(X)
X = self.pool2(X)
X = self.flatten(X)
X = self.d1(X)
X = self.dropout(X, training=training)
return self.d2(X)

# 实例化模型，定义损失函数和优化器
model = Deep_CNN_Model()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# 定义训练和测试过程中的度量指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

# 定义单批次的训练和预测操作
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
# 计算模型预测结果和损失函数值
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
# 计算梯度并优化模型参数
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# 更新训练过程中的度量指标
train_loss(loss)
train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
# 计算模型预测结果和损失函数值
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
# 更新测试过程中的度量指标
test_loss(t_loss)
test_accuracy(labels, predictions)

# 执行完整的训练过程
EPOCHS = 10
for epoch in range(EPOCHS):
# 在每轮训练开始时，重置度量指标的值
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()

# 遍历训练数据集中的每个mini_batch进行训练
for images, labels in train_ds:
train_step(images, labels)

# 遍历测试数据集中的每个mini_batch进行预测
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)

# 输出每轮训练和测试过程中的度量指标
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))

DeepFM模型tensorflow代码

以下是 DeepFM 模型的 TensorFlow 2.0 实现代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, Concatenate
from tensorflow.keras.regularizers import l2
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

class DeepFM:
    def __init__(self, feature_dim, embedding_dim=8, hidden_units=(32, 32), dropout_rate=0.5, l2_reg=0.01, learning_rate=0.001):
        self.feature_dim = feature_dim
        self.embedding_dim = embedding_dim
        self.hidden_units = hidden_units
        self.dropout_rate = dropout_rate
        self.l2_reg = l2_reg
        self.learning_rate = learning_rate

    def build(self):
        input_layer = Input((self.feature_dim,))
        embedding_layer = tf.keras.layers.Embedding(self.feature_dim, self.embedding_dim)(input_layer)
        embedding_layer = tf.keras.layers.Flatten()(embedding_layer)

        linear_layer = tf.keras.layers.Dense(units=1, activation=None)(input_layer)

        fm_layer = tf.keras.layers.GlobalAveragePooling1D()(embedding_layer)

        deep_layer = tf.keras.layers.Dense(units=self.hidden_units[0], activation='relu')(embedding_layer)
        deep_layer = tf.keras.layers.Dropout(self.dropout_rate)(deep_layer)
        for units in self.hidden_units[1:]:
            deep_layer = tf.keras.layers.Dense(units=units, activation='relu')(deep_layer)
            deep_layer = tf.keras.layers.Dropout(self.dropout_rate)(deep_layer)

        deep_fm_layer = Concatenate()([fm_layer, deep_layer])
        output_layer = tf.keras.layers.Dense(units=1, activation='sigmoid', kernel_regularizer=l2(self.l2_reg))(deep_fm_layer)

        model = Model(inputs=input_layer, outputs=output_layer)

        optimizer = Adam(lr=self.learning_rate)
        model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])

        return model

其中，`feature_dim` 表示特征的维度，`embedding_dim` 表示嵌入层的维度，`hidden_units` 表示深度部分的神经元数量，`dropout_rate` 表示 dropout 比例，`l2_reg` 表示 L2 正则项系数，`learning_rate` 表示学习率。在 `build()` 方法里，我们首先定义了输入层和嵌入层，然后分别计算了线性部分、FM 部分和深度部分的结果，最后将 FM 和深度部分的结果拼接起来，并通过一个全连接层输出最终结果。这里使用了 Adam 优化器和二分类交叉熵损失函数，并编译了模型。