三種創(chuàng)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()

為什么需要三種創(chuàng)建模式，三者有什么區(qū)別

從上往下一種比一種更接近底層，可以任意調(diào)整參數(shù)

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