函数工作流 FunctionGraph-在代码中导入tensorflow并使用

时间：2023-11-01 16:16:37

函数工作流 FunctionGraph 首先在FunctionGraph页面将tensorflow添加为公共依赖

在代码中导入tensorflow并使用

import jsonimport random# 导入 TensorFlow 依赖库import tensorflow as tfdef 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

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