tf model create

三种创建model方式

import numpy as np
import tensorflow as tf
from tensorflow import kerasrows = 10000
columns = 100
emb_size = 5
words_length = 50000train_x = np.random.random(size=(rows, columns, emb_size))
train_y = np.random.randint(low=0, high=2, size=(rows, 1))

1

model = keras.Sequential(name="test1")model.add(keras.layers.Input(shape=(columns, emb_size)))
model.add(keras.layers.SimpleRNN(units=10))
model.add(keras.layers.Dense(1))model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(train_x, train_y, epochs=10, batch_size=100)print("-------------------------------------------------")model = Sequential([keras.layers.Input(shape=(columns, emb_size)),keras.layers.SimpleRNN(units=10),keras.layers.Dense(1)]
)
model.compile(loss="mse", optimizer="sgd")
model.fit(train_x, train_y)

2

x = keras.layers.Input(shape=(columns, emb_size))y = keras.layers.SimpleRNN(units=10)(x)
y = keras.layers.Dense(1)(y)model = keras.Model(inputs=x, outputs=y)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit(train_x, train_y, epochs=10, batch_size=100)

3

class MyModel(keras.layer.Model):def __init__(self):passdef call(self, input):passmodel = MyModel()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit()

为什么需要三种创建模式，三者有什么区别

从上往下一种比一种更接近底层，可以任意调整参数

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