我试图在Tensorflow中重现深度学习回归结果 . 如果我使用sklearn的MLPRegressor类训练神经网络,我会得到98%验证的非常好的结果 .
MLPRegressor:
我试图在Tensorflow中重现该模型 . 通过在Tensorflow模型中复制MLPRegressor类的默认值 . 但是我无法得到相同的结果 . 我大部分时间只得到75% .
我的TF型号:
tf.reset_default_graph()
graph = tf.Graph()
n_input = 3 # n variables
n_hidden_1 = 100
n_hidden_2 = 1
n_output = 1
beta = 0.001
learning_rate = 0.001
with graph.as_default():
tf_train_feat = tf.placeholder(tf.float32, shape=(None, n_input))
tf_train_label = tf.placeholder(tf.float32, shape=(None))
tf_test_feat = tf.constant(test_feat, tf.float32)
"""
Weights and biases. The weights matix' columns will be the output vector.
* ndarray([rows, columns])
* ndarray([in, out])
tf.placeholder(None) and tf.placeholder([None, 3]) means that the row's size is not set. In the second
placeholder the columns are prefixed at 3.
"""
W = {
"layer_1": tf.Variable(tf.truncated_normal([n_input, n_hidden_1])),
"layer_2": tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),
"layer_3": tf.Variable(tf.truncated_normal([n_hidden_2, n_output])),
}
b = {
"layer_1": tf.Variable(tf.zeros([n_hidden_1])),
"layer_2": tf.Variable(tf.zeros([n_hidden_2])),
}
def computation(X):
layer_1 = tf.nn.relu(tf.matmul(X, W["layer_1"]) + b["layer_1"])
layer_2 = tf.nn.relu(tf.matmul(layer_1, W["layer_2"]) + b["layer_2"])
return layer_2
tf_prediction = computation(tf_train_feat)
tf_test_prediction = computation(tf_test_feat)
tf_loss = tf.reduce_mean(tf.pow(tf_train_label - tf_prediction, 2))
tf_loss = tf.reduce_mean( tf_loss + beta * tf.nn.l2_loss(W["layer_2"]) )
tf_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
#tf_optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(tf_loss)
init = tf.global_variables_initializer()
我的TF Session :
def accuracy(y_pred, y):
a = 0
for i in range(y.shape[0]):
a += abs(1 - y_pred[i][0] / y[i])
return round((1 - a / y.shape[0]) * 100, 3)
def accuracy_tensor(y_pred, y):
a = 0
for i in range(y.shape[0]):
a += abs(1 - y_pred[i][0] / y[i])
return round((1 - a / y.shape[0]) * 100, 3)
# Shuffles two arrays.
def shuffle_in_unison(a, b):
assert len(a) == len(b)
shuffled_a = np.empty(a.shape, dtype=a.dtype)
shuffled_b = np.empty(b.shape, dtype=b.dtype)
permutation = np.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b
train_epoch = int(5e4)
batch = int(200)
n_batch = int(X.shape[0] // batch)
prev_acc = 0
stable_count = 0
session = tf.InteractiveSession(graph=graph)
session.run(init)
print("Initialized.\n No. of epochs: %d.\n No. of batches: %d." % (train_epoch, n_batch))
for epoch in range(train_epoch):
offset = (epoch * n_batch) % (Y.shape[0] - n_batch)
for i in range(n_batch):
x = X[offset:(offset + n_batch)]
y = Y[offset:(offset + n_batch)]
x, y = shuffle_in_unison(x, y)
feed_dict = {tf_train_feat: x, tf_train_label: y}
_, l, pred, pred_label = session.run([tf_optimizer, tf_loss, tf_prediction, tf_train_label], feed_dict=feed_dict)
if epoch % 1 == 0:
print("Epoch: %d. Batch' loss: %f" %(epoch, l))
test_pred = tf_test_prediction.eval(session=session)
acc_test = accuracy(test_pred, test_label)
acc_train = accuracy_tensor(pred, pred_label)
print("Accuracy train set %s%%" % acc_train)
print("Accuracy test set: %s%%" % acc_test)
我在Tensorflow代码中遗漏了什么吗?谢谢!
1 回答
除非你有充分的理由不使用它们,否则回归应该有线性输出单位 . 我在一段时间后遇到了类似的问题,结果使用线性输出和线性隐藏单元,这似乎反映了我的情况下的mlpregressor .
在chapter 6中,Goodfellow的Deep Learning Book中有一个很棒的部分,从第181页开始,它涉及激活功能 .
至少尝试一下输出层