tensorflow2.0实现复杂神经网络(多输入多输出nn，Resnet)

# 求和
layers.Add(inputs)
# 求差
layers.Subtract(inputs)

input1 = keras.layers.Input(shape=(16,))
x1 = keras.layers.Dense(8, activation='relu')(input1)
input2 = keras.layers.Input(shape=(32,))
x2 = keras.layers.Dense(8, activation='relu')(input2)
added = keras.layers.add([x1, x2])

out = keras.layers.Dense(4)(added)
model = keras.models.Model(inputs=[input1, input2], outputs=out)

# 输入张量的逐元素乘积(对应位置元素相乘，输入维度必须相同)
layers.multiply(inputs)
# 输入张量样本之间的点积
layers.dot(inputs, axes, nORMalize=False) 

x = np.arange(10).reshape(1, 5, 2)

y = np.arange(10, 20).reshape(1, 2, 5)

# 三维的输入做dot通常像这样指定axes,表示矩阵的第一维度和第二维度参与矩阵乘法，第0维度是batchsize
tf.keras.layers.Dot(axes=(1, 2))([x, y])
# 输出如下：
<tf.Tensor: shape=(1, 2, 2), dtype=int64, numpy=
array([[[260, 360],
  [320, 445]]])>

x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
dotted = tf.keras.layers.Dot(axes=1)([x1, x2])
dotted.shape
TensorShape([5, 1])

# 所有输入张量通过 axis 轴串联起来的输出张量。
layers.add(inputs,axis=-1)

x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
concatted = tf.keras.layers.Concatenate()([x1, x2])
concatted.shape
TensorShape([5, 16])

input1 = tf.keras.layers.Input(shape=(16,))
x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
input2 = tf.keras.layers.Input(shape=(32,))
x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
avg = tf.keras.layers.Average()([x1, x2])
# x_1 x_2 的均值作为输出
print(avg)
# <tf.Tensor 'average/Identity:0' shape=(None, 8) dtype=float32>

out = tf.keras.layers.Dense(4)(avg)
model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)

num_tags = 12
num_Words = 10000
num_departments = 4

title_input = keras.Input(shape=(None,), name="title") # Variable-length sequence of ints
body_input = keras.Input(shape=(None,), name="body") # Variable-length sequence of ints
tags_input = keras.Input(shape=(num_tags,), name="tags") # Binary vectors of size `num_tags`

title_features = layers.Embedding(num_words,64)(title_input)
body_features = layers.Embedding(num_words,64)(body_input)

title_features = layers.LSTM(128)(title_features)
body_features = layers.LSTM(32)(body_features)

x = layers.concatenate([title_features, body_features, tags_input])

# 输出1，回归问题
priority_pred = layers.Dense(1,name="priority")(x)

# 输出2，分类问题
department_pred = layers.Dense(num_departments,name="department")(x)

model = keras.Model(
 inputs=[title_input, body_input, tags_input],
 outputs=[priority_pred, department_pred],
)

model.compile(
 optimizer=keras.optimizers.RMSprop(1e-3),
 loss={
  "priority": keras.losses.BinaryCrossentropy(from_logits=True),
  "department": keras.losses.CateGoricalCrossentropy(from_logits=True),
 },
 loss_weights=[1.0, 0.2],
)

# Dummy input data
title_data = np.random.randint(num_words, size=(1280, 10))
body_data = np.random.randint(num_words, size=(1280, 100))
tags_data = np.random.randint(2, size=(1280, num_tags)).astype("float32")

# Dummy target data
priority_targets = np.random.random(size=(1280, 1))
dept_targets = np.random.randint(2, size=(1280, num_departments))

# 通过字典的形式将数据fit到模型
model.fit(
 {"title": title_data, "body": body_data, "tags": tags_data},
 {"priority": priority_targets, "department": dept_targets},
 epochs=2,
 batch_size=32,
)

# 实现第一个块
_input = keras.Input(shape=(32,32,3))
x = layers.Conv2D(32,3,activation='relu')(_input)
x = layers.Conv2D(64,3,activation='relu')(x)
block1_output = layers.MaxPooling2D(3)(x)

# 实现第二个块
x = layers.Conv2D(64,3,padding='same',activation='relu')(block1_output)
x = layers.Conv2D(64,3,padding='same',activation='relu')(x)
block2_output = layers.add([x,block1_output])


# 实现第三个块
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block2_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_3_output = layers.add([x, block2_output])

# 进入全连接层
x = layers.Conv2D(64,3,activation='relu')(block_3_output)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(256, activation="relu")(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10)(x)

model = keras.Model(_input,outputs,name='resnet')

model.compile(
 optimizer=keras.optimizers.RMSprop(1e-3),
 loss='sparse_categorical_crossentropy',
 metrics=["acc"],
)

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# 归一化
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
model.fit(tf.expand_dims(x_train,-1), y_train, batch_size=64, epochs=1, validation_split=0.2)

y_train = keras.utils.to_categorical(y_train, 10)