我对使用python进行机器学习真的很陌生，我已经在使用Lenet的交通信号识别程序上使用主成分分析进行了测试，但是与正常的预处理图像训练（验证精度约为94％）相反，我尝试在PCA上执行相同的图像，但由于验证准确度未高于4％，因此无法正常工作。它确实工作了一次，但是我碰巧做了一些修改，导致了这个问题。
有人知道在这种情况下如何使精度不恒定吗？
我在Google合作实验室的GPU上运行它，并尝试增加和减少神经网络的最大组件数和学习率。他们都没有工作。
这里是神经网络模型： LaNet类：
number这是预处理代码
def __init__(self, n_out=43, mu=0, sigma=0.1, learning_rate=0.001):
# Hyperparameters
self.mu = mu
self.sigma = sigma
# Layer 1 (Convolutional): Input = 32x32x1. Output = 28x28x6.
self.filter1_width = 5
self.filter1_height = 5
self.input1_channels = 1
self.conv1_output = 6
# Weight and bias
self.conv1_weight = tf.Variable(tf.truncated_normal(
shape=(self.filter1_width, self.filter1_height, self.input1_channels, self.conv1_output),
mean = self.mu, stddev = self.sigma))
self.conv1_bias = tf.Variable(tf.zeros(self.conv1_output))
# Apply Convolution
self.conv1 = tf.nn.conv2d(x, self.conv1_weight, strides=[1, 1, 1, 1], padding='VALID') + self.conv1_bias
# Activation:
self.conv1 = tf.nn.relu(self.conv1)
# Pooling: Input = 28x28x6. Output = 14x14x6.
self.conv1 = tf.nn.max_pool(self.conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Layer 2 (Convolutional): Output = 10x10x16.
self.filter2_width = 5
self.filter2_height = 5
self.input2_channels = 6
self.conv2_output = 16
# Weight and bias
self.conv2_weight = tf.Variable(tf.truncated_normal(
shape=(self.filter2_width, self.filter2_height, self.input2_channels, self.conv2_output),
mean = self.mu, stddev = self.sigma))
self.conv2_bias = tf.Variable(tf.zeros(self.conv2_output))
# Apply Convolution
self.conv2 = tf.nn.conv2d(self.conv1, self.conv2_weight, strides=[1, 1, 1, 1], padding='VALID') + self.conv2_bias
# Activation:
self.conv2 = tf.nn.relu(self.conv2)
# Pooling: Input = 10x10x16. Output = 5x5x16.
self.conv2 = tf.nn.max_pool(self.conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Flattening: Input = 5x5x16. Output = 400.
self.fully_connected0 = flatten(self.conv2)
# Layer 3 (Fully Connected): Input = 400. Output = 120.
self.connected1_weights = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = self.mu, stddev = self.sigma))
self.connected1_bias = tf.Variable(tf.zeros(120))
self.fully_connected1 = tf.add((tf.matmul(self.fully_connected0, self.connected1_weights)), self.connected1_bias)
# Activation:
self.fully_connected1 = tf.nn.relu(self.fully_connected1)
# Layer 4 (Fully Connected): Input = 120. Output = 84.
self.connected2_weights = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = self.mu, stddev = self.sigma))
self.connected2_bias = tf.Variable(tf.zeros(84))
self.fully_connected2 = tf.add((tf.matmul(self.fully_connected1, self.connected2_weights)), self.connected2_bias)
# Activation.
self.fully_connected2 = tf.nn.relu(self.fully_connected2)
# Layer 5 (Fully Connected): Input = 84. Output = 43.
self.output_weights = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = self.mu, stddev = self.sigma))
self.output_bias = tf.Variable(tf.zeros(43))
self.logits = tf.add((tf.matmul(self.fully_connected2, self.output_weights)), self.output_bias)
# Training operation
self.one_hot_y = tf.one_hot(y, n_out)
self.cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.one_hot_y)
self.loss_operation = tf.reduce_mean(self.cross_entropy)
self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
self.training_operation = self.optimizer.minimize(self.loss_operation)
# Accuracy operation
self.correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.one_hot_y, 1))
self.accuracy_operation = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
# Saving all variables
self.saver = tf.train.Saver()
def y_predict(self, X_data, BATCH_SIZE=64):
num_examples = len(X_data)
y_pred = np.zeros(num_examples, dtype=np.int32)
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x = X_data[offset:offset+BATCH_SIZE]
y_pred[offset:offset+BATCH_SIZE] = sess.run(tf.argmax(self.logits, 1),
feed_dict={x:batch_x, keep_prob:1, keep_prob_conv:1})
return y_pred
def evaluate(self, X_data, y_data, BATCH_SIZE=64):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(self.accuracy_operation,
feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0, keep_prob_conv: 1.0 })
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
现在这是PCA计算命令以及模型训练
def gray_scale(image):
return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
def local_histo_equalize(image):
"""
Apply local histogram equalization to grayscale images.
Parameters:
image: A grayscale image.
"""
kernel = morp.disk(30)
img_local = rank.equalize(image, selem=kernel)
return img_local
def image_normalize(image):
image = np.divide(image, 255)
return image
n_training = X_train.shape
normalized_images = np.zeros((n_training[0], n_training[1], n_training[2]))
for i, img in enumerate(equalized_images):
normalized_images[i] = image_normalize(img)
def preprocess(data):
gray_images = list(map(gray_scale, data))
equalized_images = list(map(local_histo_equalize, gray_images))
n_training = data.shape
normalized_images = np.zeros((n_training[0], n_training[1], n_training[2]))
for i, img in enumerate(equalized_images):
normalized_images[i] = image_normalize(img)
normalized_images = normalized_images[..., None]
return normalized_images
我使用的数据集被转换为泡菜数据，它由34799个训练图像，12630个测试图像和4410个验证图像组成，所有颜色均为（32,32,3）
很抱歉，代码量很大，但是我真的希望任何人都可以帮忙解决这个问题，因为这对我的最终论文项目非常重要。 感谢您的帮助。