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公共依赖Demo

函数工作流 FUNCTIONGRAPH-使用TensorFlow进行线性回归:在代码中导入tensorflow并使用

在代码中导入tensorflow并使用 import json import random # 导入 TensorFlow 依赖库 import tensorflow as tf def handler (event, context): TRUE_W = random.randint(0,9) TRUE_b = random.randint(0,9) NUM_SAMPLES = 100 X = tf.random.normal(shape=[NUM_SAMPLES, 1]).numpy() noise = tf.random.normal(shape=[NUM_SAMPLES, 1]).numpy() y = X * TRUE_W + TRUE_b + noise model = tf.keras.layers.Dense(units=1) EPOCHS = 20 LEARNING_RATE = 0.002 print("start training") for epoch in range(EPOCHS): with tf.GradientTape() as tape: y_ = model(X) loss = tf.reduce_sum(tf.keras.losses.mean_squared_error(y, y_)) grads = tape.gradient(loss, model.variables) optimizer = tf.keras.optimizers.SGD(LEARNING_RATE) optimizer.apply_gradients(zip(grads, model.variables)) print('Epoch [{}/{}], loss [{:.3f}]'.format(epoch, EPOCHS, loss)) print("finished") print(TRUE_W,TRUE_b) print(model.variables) return { "statusCode": 200, "isBase64Encoded": False, "body": json.dumps(event), "headers": { "Content-Type": "application/json" } } class Model(object): def __init__(self): self.W = tf.Variable(tf.random.uniform([1])) self.b = tf.Variable(tf.random.uniform([1])) def __call__(self, x): return self.W * x + self.b

函数工作流 FUNCTIONGRAPH 公共依赖Demo