吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第三周所有jupyter notebook文件：

吴恩达,机器学习专项课程, Advanced Learning Algorithms第三周所有Python编程文件

本次作业

Exercise 1

# UNQ_C1# GRADED CELL: eval_msedef eval_mse(y, yhat):""" Calculate the mean squared error on a data set.Args:y : (ndarray Shape (m,) or (m,1)) target value of each exampleyhat : (ndarray Shape (m,) or (m,1)) predicted value of each exampleReturns:err: (scalar) """m = len(y)err = 0.0for i in range(m):### START CODE HERE ### err += (y[i]-yhat[i])**2err = err /2/ m ### END CODE HERE ### return(err)

Exercise 2

# UNQ_C2# GRADED CELL: eval_cat_errdef eval_cat_err(y, yhat):""" Calculate the categorization errorArgs:y : (ndarray Shape (m,) or (m,1)) target value of each exampleyhat : (ndarray Shape (m,) or (m,1)) predicted value of each exampleReturns:|cerr: (scalar) """m = len(y)incorrect = 0for i in range(m):### START CODE HERE ### if y[i] != yhat[i]:incorrect += 1cerr = incorrect / m### END CODE HERE ### return(cerr)

Exercise 3

# UNQ_C3# GRADED CELL: modelimport logginglogging.getLogger("tensorflow").setLevel(logging.ERROR)tf.random.set_seed(1234)model = Sequential([### START CODE HERE ### # tf.keras.Input(shape=(2,)),Dense(120,activation='relu',name='layer1'),Dense(40,activation='relu',name='layer2'),Dense(6,activation='linear',name='layer3')### END CODE HERE ### ], name="Complex")pile(### START CODE HERE ### loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),optimizer=tf.keras.optimizers.Adam(0.01),### END CODE HERE ### )

Exercise 4

# UNQ_C4# GRADED CELL: model_stf.random.set_seed(1234)model_s = Sequential([### START CODE HERE ### Dense(6,activation='relu',name='layer1'),Dense(6,activation='linear',name='layer2') ### END CODE HERE ### ], name = "Simple")pile(### START CODE HERE ### loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),optimizer = tf.keras.optimizers.Adam(0.01),### START CODE HERE ### )

Exercise 5

# UNQ_C5# GRADED CELL: model_rtf.random.set_seed(1234)model_r = Sequential([### START CODE HERE ### Dense(120,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.1),name='layer1'),Dense(40,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.1),name='layer2'), Dense(6,activation='linear',name='layer3')### START CODE HERE ### ], name= 'aaa')pile(### START CODE HERE ### loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),optimizer = tf.keras.optimizers.Adam(0.01),### START CODE HERE ### )