我对这个领域有点陌生，所以我开始在 Keras 中修改一些模型（使用 Tensorflow 后端）。但是最近我开始迁移到纯 Tensorflow 方法，但我没有得到好的结果，这很奇怪，因为我在 Keras 中使用 TF 后端，所以我期待类似的结果。所以，最肯定的是，我在实现中遇到了一些错误，但我无法弄清楚它是什么。
我正在尝试使用 ADAM 实现一个 7 层 MLP，一个线性输出。为方便起见，我从模型中删除了所有正则化，因此我预计模型会过拟合，Keras 模型会发生这种情况，但 TF 模型不会。如果有人能指出 Tensorflow 实现中的问题，我将不胜感激。
这是 github 存储库： https ://github.com/makalaia/Tensorflow-Benchmark
凯拉斯代码：
import keras
import numpy as np
import time
import matplotlib.pyplot as plt
from keras.layers import Dense
from keras.models import Sequential
from pandas import read_csv
def calculate_rmse(real, predict):
m = len(real)
return np.sqrt(np.sum(np.power((real - predict), 2)) / m)
test_size = 150
df = read_csv('data/mastigadin.csv', header=None)
df.set_index(list(df)[0], inplace=True)
y_total = df.iloc[:, -1:].values
x_total = df.iloc[:, :-1].values
y_train = y_total[:-test_size, :]
x_train = x_total[:-test_size, :]
y_test = y_total[-test_size:, :]
x_test = x_total[-test_size:, :]
tempo = time.time()
# Neural net
epochs = 200
batch_size = 64
optmizer = keras.optimizers.Adam()
model = Sequential()
model.add(Dense(256, input_shape=(x_train.shape[1],)))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(1))
# fit
model.compile(loss='mean_squared_error', optimizer=optmizer)
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), verbose=2)
print('TIME: ' + str(time.time() - tempo))
# predict
y_trained = model.predict(x_train)
y_tested = model.predict(x_test)
# errors
error_train = calculate_rmse(y_train, y_trained)
print('TRAIN: RMSE - ' + str(error_train))
error_test = calculate_rmse(y_test, y_tested)
print('\nVAL: RMSE - ' + str(error_test))
# plot
plt.plot(y_total, label='REAL DATA')
plt.plot(y_trained, label='TRAINED DATA')
plt.plot(range(len(y_train), len(y_total)), y_tested, label='TEST DATA')
plt.legend()
plt.title('KERAS')
plt.show()
张量流代码：
import numpy as np
import tensorflow as tf
import time
import matplotlib.pyplot as plt
from pandas import read_csv
def calculate_rmse(real, predict):
m = len(real)
return np.sqrt(np.sum(np.power((real - predict), 2)) / m)
test_size = 150
df = read_csv('data/mastigadin.csv', header=None)
df.set_index(list(df)[0], inplace=True)
y_total = df.iloc[:, -1:].values
x_total = df.iloc[:, :-1].values
y_train = y_total[:-test_size, :]
x_train = x_total[:-test_size, :]
y_test = y_total[-test_size:, :]
x_test = x_total[-test_size:, :]
n_samples = x_train.shape[0]
tempo = time.time()
epochs = 200
batch_size = 64
n_input = 36
n_output = 1
n_hidden = 256
# tf Graph input
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_output])
# Store layers weight & bias
weights = {
'h1': tf.get_variable('h1', shape=[n_input, n_hidden]),
'h2': tf.get_variable('h2', shape=[n_hidden, n_hidden]),
'h3': tf.get_variable('h3', shape=[n_hidden, n_hidden]),
'h4': tf.get_variable('h4', shape=[n_hidden, n_hidden]),
'h5': tf.get_variable('h5', shape=[n_hidden, n_hidden]),
'h6': tf.get_variable('h6', shape=[n_hidden, n_hidden]),
'h7': tf.get_variable('h7', shape=[n_hidden, n_hidden]),
'out': tf.Variable(tf.random_normal([n_hidden, n_output]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden])),
'b2': tf.Variable(tf.random_normal([n_hidden])),
'b3': tf.Variable(tf.random_normal([n_hidden])),
'b4': tf.Variable(tf.random_normal([n_hidden])),
'b5': tf.Variable(tf.random_normal([n_hidden])),
'b6': tf.Variable(tf.random_normal([n_hidden])),
'b7': tf.Variable(tf.random_normal([n_hidden])),
'out': tf.Variable(tf.random_normal([n_output]))
}
# Create model
def multilayer_perceptron(x):
layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))
layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))
layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))
layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))
layer_7 = tf.nn.relu(tf.add(tf.matmul(layer_6, weights['h7']), biases['b7']))
out_layer = tf.matmul(layer_7, weights['out']) + biases['out']
return out_layer
# Construct model
pred = multilayer_perceptron(X)
# Define loss and optimizer
cost = tf.reduce_mean(tf.squared_difference(pred, Y))
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(cost)
# Initializing the variables
init = tf.global_variables_initializer()
display_step = 1
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(epochs):
avg_cost = 0.
total_batch = int(n_samples / batch_size)
# Loop over all batches
tp = time.time()
for i in range(total_batch):
batch_x = x_train[i * batch_size:(i + 1) * batch_size]
batch_y = y_train[i * batch_size:(i + 1) * batch_size]
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([train_op, cost], feed_dict={X: batch_x, Y: batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost={:.9f}".format(avg_cost), "TIME: %.2f" % (time.time() - tp))
print('TIME: ' + str(time.time() - tempo))
# Test model
y_trained = sess.run(pred, feed_dict={X: x_train})
y_tested = sess.run(pred, feed_dict={X: x_test})
error_train = calculate_rmse(y_train, y_trained)
print('TRAIN: RMSE - ' + str(error_train))
error_test = calculate_rmse(y_test, y_tested)
print('\nVAL: RMSE - ' + str(error_test))
plt.plot(y_total, label='REAL DATA')
plt.plot(y_trained, label='TRAINED DATA')
plt.plot(range(len(y_train), len(y_total)), y_tested, label='TEST DATA')
plt.legend()
plt.title('TENSORFLOW')
plt.show()