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_mse
def 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])**2          err = err /2/ m  ### END CODE HERE ### return(err)

Exercise 2

# UNQ_C2
# GRADED CELL: eval_cat_err
def 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: model
import logging
logging.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"
)
model.compile(### 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"
)
model_s.compile(### 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'
)
model_r.compile(### START CODE HERE ### loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),optimizer = tf.keras.optimizers.Adam(0.01),### START CODE HERE ###
)