问使用TensorFlow的梯度下降要比基本的Python实现慢得多，为什么？

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#%% General imports
import numpy as np
import timeit
import tensorflow.compat.v1 as tf


#%% Get input data
# Generate simulated input data
x_data_input = np.arange(100, step=0.1)
y_data_input = x_data_input + 20 * np.sin(x_data_input/10) + 15


#%% Define tensorflow model
# Define data size
n_samples = x_data_input.shape[0]

# Tensorflow is finicky about shapes, so resize
x_data = np.reshape(x_data_input, (n_samples, 1))
y_data = np.reshape(y_data_input, (n_samples, 1))

# Define placeholders for input
X = tf.placeholder(tf.float32, shape=(n_samples, 1), name="tf_x_data")
Y = tf.placeholder(tf.float32, shape=(n_samples, 1), name="tf_y_data")

# Define variables to be learned
with tf.variable_scope("linear-regression", reuse=tf.AUTO_REUSE): #reuse= True | False | tf.AUTO_REUSE
    W = tf.get_variable("weights", (1, 1), initializer=tf.constant_initializer(0.0))
    b = tf.get_variable("bias", (1,), initializer=tf.constant_initializer(0.0))

# Define loss function    
Y_pred = tf.matmul(X, W) + b
loss = tf.reduce_sum((Y - Y_pred) ** 2 / n_samples)  # Quadratic loss function


# %% Solve tensorflow model
#Define algorithm parameters
total_iterations = 1e5  # Defines total training iterations

#Construct TensorFlow optimizer
with tf.variable_scope("linear-regression", reuse=tf.AUTO_REUSE): #reuse= True | False | tf.AUTO_REUSE
    opt = tf.train.GradientDescentOptimizer(learning_rate = 1e-4)
    opt_operation = opt.minimize(loss, name="GDO")

#To measure execution time
time_start = timeit.default_timer()

with tf.Session() as sess:
    #Initialize variables
    sess.run(tf.global_variables_initializer())
    
    #Train variables
    for index in range(int(total_iterations)):
        _, loss_val_tmp = sess.run([opt_operation, loss], feed_dict={X: x_data, Y: y_data})
    
    #Get final values of variables
    W_val, b_val, loss_val = sess.run([W, b, loss], feed_dict={X: x_data, Y: y_data})
      
#Print execution time      
time_end = timeit.default_timer()
print('')
print("Time to execute code: {0:0.9f} sec.".format(time_end - time_start))
print('')


# %% Print results
print('')
print('Iteration = {0:0.3f}'.format(total_iterations))
print('W_val = {0:0.3f}'.format(W_val[0,0]))
print('b_val = {0:0.3f}'.format(b_val[0]))
print('')

#%% General imports
import numpy as np
import timeit


#%% Get input data
# Define input data
x_data_input = np.arange(100, step=0.1)
y_data_input = x_data_input + 20 * np.sin(x_data_input/10) + 15


#%% Define Gradient Descent (GD) model
# Define data size
n_samples = x_data_input.shape[0]

#Initialize data
W = 0.0  # Initial condition
b = 0.0  # Initial condition

# Compute initial loss
y_gd_approx = W*x_data_input+b
loss = np.sum((y_data_input - y_gd_approx)**2)/n_samples  # Quadratic loss function


#%% Execute Gradient Descent algorithm
#Define algorithm parameters
total_iterations = 1e5  # Defines total training iterations
GD_stepsize = 1e-4  # Gradient Descent fixed step size

#To measure execution time
time_start = timeit.default_timer()

for index in range(int(total_iterations)):
    #Compute gradient (derived manually for the quadratic cost function)
    loss_gradient_W = 2.0/n_samples*np.sum(-x_data_input*(y_data_input - y_gd_approx))
    loss_gradient_b = 2.0/n_samples*np.sum(-1*(y_data_input - y_gd_approx))
    
    #Update trainable variables using fixed step size gradient descent
    W = W - GD_stepsize * loss_gradient_W
    b = b - GD_stepsize * loss_gradient_b
    
    #Compute loss
    y_gd_approx = W*x_data_input+b
    loss = np.sum((y_data_input - y_gd_approx)**2)/x_data_input.shape[0]

#Print execution time 
time_end = timeit.default_timer()
print('')
print("Time to execute code: {0:0.9f} sec.".format(time_end - time_start))
print('')


# %% Print results
print('')
print('Iteration = {0:0.3f}'.format(total_iterations))
print('W_val = {0:0.3f}'.format(W))
print('b_val = {0:0.3f}'.format(b))
print('')