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1 Dense Layer

目的：构建Dense层，对比两种方法实现的区别

2 对比原始的add layer方法和继承方法的不同

2.1 global config

import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras.layers import Input, LSTM, Denseimport numpy as npnp.random.seed(1)rows = 10000  	# 样本数columns = 100	# 特征数train_x1 = np.random.random(size=(int(rows/2), columns))train_y1 = np.random.choice([0], size=(int(rows/2), 1))train_x2 = np.random.random(size=(int(rows/2), columns))+1train_y2 = np.random.choice([1], size=(int(rows/2), 1))train_x = np.vstack((train_x1, train_x2))train_y = np.vstack((train_y1, train_y2))units = 5	# 自定义cell个数

2.1 用add实现

tf.random.set_seed(1)		# 固定随机值model1 = keras.Sequential()model1.add(Input(shape=(columns,)))model1.add(Dense(units=units))model1.compile(optimizer="adam", loss="mse", metrics=["accuracy"])model1.fit(train_x, train_y, epochs=10)model1.predict(train_x)[-1][-1]l1 = model1.layers[0]w1, b1 = l1.get_weights()

api中的参数分几部分

1. initializer 初始项，初始化参数
2. regularizer 正则项，选择不同正则模式L1L2
3. constraint 约束项，非负约束或者最大模约束

2.2 用继承实现

tf.random.set_seed(1)		# 固定随机值class MyDenseLayer(keras.layers.Layer):	def __init__(self, num_outputs):		super(MyDenseLayer, self).__init__()		self.num_outputs = num_outputs	def build(self, input_shape):		self.kernel = self.add_weight(name="kernel", shape=[int(input_shape[-1]), self.num_outputs])		self.bias = self.add_weight(name="bias", shape=[self.num_outputs, ], initializer=keras.initializers.zeros)		self.build = True	def call(self, input):		return tf.matmul(input, self.kernel) + self.biasmodel2 = keras.Sequential()model2.add(Input(shape=(columns,)))model2.add(MyDenseLayer(units))		model2.compile(loss="mse", optimizer="adam", metrics=['accuracy'])model2.fit(train_x, train_y, epochs=10)model2.predict(train_x)[-1][-1]l2 = model2.layers[0]w2, b2 = l2.get_weights()

3 有权重的对比

3.1 用自带add_weight方法自定义权重

自定义初始的权重和偏移项。为什么要定义这两个方法？为了用add_weight方法初始权重和偏移项

def w_init(shape, dtype=tf.float32):	return tf.random.normal(shape=shape, dtype=dtype)def b_init(shape, dtype=tf.float32):	return tf.zeros(shape=shape, dtype=dtype)

3.2 用add实现

tf.random.set_seed(1)		# 固定随机值model3 = keras.Sequential()model3.add(Input(shape=(columns,)))model3.add(Dense(units=units, kernel_initializer=w_init, bias_initializer=b_init))	# 需要固定weights		model3.compile(optimizer="adam", loss="mse", metrics=["accuracy"])model3.fit(train_x, train_y, epochs=10)model3.predict(train_x)[-1][-1]l3 = model3.layers[0]w3, b3 = l3.get_weights()

3.3 用继承实现

tf.random.set_seed(1)		# 固定随机值class MyDenseLayer(keras.layers.Layer):	def __init__(self, num_outputs):		super(MyDenseLayer, self).__init__()		self.num_outputs = num_outputs	def build(self, input_shape):		self.kernel = self.add_weight(initializer=w_init, shape=(input_shape[-1], self.num_outputs), dtype=tf.float32)	# 自定义权重		self.bias = self.add_weight(initializer=b_init, shape=(self.num_outputs,), dtype=tf.float32)		# 自定义偏移项	def call(self, input):		return tf.matmul(input, self.kernel) + self.biasmodel4 = keras.Sequential()model4.add(Input(shape=(columns,)))model4.add(MyDenseLayer(units))		model4.compile(loss="mse", optimizer="adam", metrics=['accuracy'])model4.fit(train_x, train_y, epochs=10)model4.predict(train_x)[-1][-1]l4 = model4.layers[0]w4, b4 = l4.get_weights()

4 用自定义矩阵为权重矩阵

4.1 初始化权重和偏移项矩阵

tf.random.set_seed(1)w = tf.random.normal(shape=(columns, units), dtype=tf.float32)b = tf.zeros(shape=(units,), dtype=tf.float32)def w_init(shape, dtype=tf.float32):	return wdef b_init(shape, dtype=tf.float32):	return b

4.2 用add实现

tf.random.set_seed(1)		# 固定随机值model5 = keras.Sequential()model5.add(Input(shape=(columns,)))model5.add(Dense(units=units, kernel_initializer=w_init, bias_initializer=b_init))		model5.compile(loss="mse", optimizer="adam", metrics=['accuracy'])model5.fit(train_x, train_y, epochs=10)model5.predict(train_x)[-1][-1]

4.3 用继承实现

不用Layer.add_weight方法，自己实现一个权重矩阵，然后用权重矩阵作为初始化，进行训练

tf.random.set_seed(1)		# 固定随机值class MyDenseLayer(keras.layers.Layer):	def __init__(self, num_outputs):		super(MyDenseLayer, self).__init__()		self.num_outputs = num_outputs	def build(self, input_shape):		self.kernel = tf.Variable(w, trainable=True)		self.bias = tf.Variable(b, trainable=True)	def call(self, input):		return tf.matmul(input, self.kernel) + self.biasmodel6 = keras.Sequential()model6.add(Input(shape=(columns,)))model6.add(MyDenseLayer(units))		model6.compile(loss="mse", optimizer="adam", metrics=['accuracy'])model6.fit(train_x, train_y, epochs=10)model6.predict(train_x)[-1][-1]

5 用numpy实现

5.1 初始化权重和偏移项矩阵

tf.random.set_seed(1)train_x = np.ones(shape=(rows, columns), dtype="float32")	# 这里一定要dtype一致，否则numpy与keras计算结果会有差异，我这里统一使用float32train_y = np.vstack([np.ones(shape=(int(rows/2), 1), dtype="float32"), np.zeros(shape=(int(rows/2),1), dtype="float32")])w = tf.random.normal(shape=(columns, 1), dtype=tf.float32)b = tf.zeros(shape=(1,), dtype=tf.float32)def w_init(shape, dtype=tf.float32):	return tf.convert_to_tensor(w, dtype=tf.float32)def b_init(shape, dtype=tf.float32):	return tf.convert_to_tensor(b, dtype=tf.float32)

5.2 add实现，有激活函数

tf.random.set_seed(1)		# 固定随机值model7 = keras.Sequential()model7.add(Input(shape=(columns,)))model7.add(Dense(units=1, kernel_initializer=w_init, bias_initializer=b_init, activation="sigmoid"))h1 = model7.predict(train_x)model7.compile(loss="mse", optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate), metrics=['accuracy'])model7.fit(train_x, train_y, epochs=1, batch_size=rows)	#这里要注意batch_size要用BatchGD，因为numpy实现时没有用batch，用的是全量数据更新w1, b1 = model7.layers[0].weights

5.3 tf 实现

tf.random.set_seed(1)		# 固定随机值x = tf.Variable(train_x, dtype=tf.float32)w2 = wb2 = bwith tf.GradientTape(persistent=True) as tape:	tape.watch([w2, b2])	y_pred = 1/(1+tf.math.exp(-1*tf.matmul(x, w2)+b2))	loss = tf.math.reduce_mean(tf.math.square(tf.subtract(y_pred, train_y)))dw2 = tape.gradient(target=loss, sources=w2)db2 = tape.gradient(target=loss, sources=b2)w2 = w2 - dw2*learning_rateb2 = b2 - db2*learning_rate

5.4 numpy实现

import numpy as npclass MyModel:    def __init__(self, w, b, learning_rate):        self.w = w        self.b = b        self.learning_rate = learning_rate    def fit(self, train_x, train_y, epochs, batch_size):        self.x = train_x        self.y = train_y        for epoch in range(epochs):            print(f"epoch {epoch}")            self.forward()  # 正向传播            self.get_loss()            self.backward()    def forward(self):        self.h3 = self.sigmoid(np.dot(self.x, self.w) + self.b)    def backward(self):        learning_rate = 0.01        dw3 = np.dot(self.x.T, 2*(self.h3 - self.y)*self.h3*(1-self.h3)/train_x.shape[0])  # loss对w的求导        db3 = np.dot(np.ones(shape=(1, rows)), 2*(self.h3 - self.y)*self.h3*(1-self.h3)/train_x.shape[0])  # loss对b的求导        self.w -= dw3 * learning_rate        self.b -= db3 * learning_rate    def sigmoid(self, x):        return 1 / (1 + np.exp(-x))    def get_loss(self):        loss = np.sum((np.square(self.h3-self.y)), axis=0)/rows        print(f"loss {loss}")    def predict(self):        passmodel8 = MyModel(w, b, learning_rate)model8.fit(train_x, train_y, epochs=1, batch_size=rows)w3 = model8.wb3 = model8.b

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