我试图从工具锤上取下螺丝 . 已经从谷歌抓取了500/500张图片（数据不够？） . 所以它的图像分类很简单 . 但是，我做得更差，准确率为59％ . 有谁知道为什么会这样？
Write images to dataframes
首先，图像被转换为灰度cv2.imread（img，0） . 然后将爬行的图像写入数据帧 . 标签是根据文件名创建的 . 图像大小调整为300 x 300像素 .
import cv2
import os
import random
import matplotlib.pylab as plt
import matplotlib.pyplot as plot
from glob import glob
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
PATH = os.path.abspath(os.path.join('crawler'))
SOURCE_IMAGES = os.path.join(PATH, "data", "all")
images = glob(os.path.join(SOURCE_IMAGES, "*.jpg"))
def proc_images():
x = [] # images as arrays
y = [] # labels
WIDTH = 300
HEIGHT = 300
count = 0
for img in images:
count = count + 1
for j in range(count -1):
column = []
for i in range(2):
column.append(0)
y.append(column)
print(np.array(y).shape)
row_number = 0
for img in images:
full_size_image = None
base = os.path.basename(img)
# Read and resize image
full_size_image = cv2.imread(img, 0)
if full_size_image is not None:
good_image = cv2.resize(full_size_image, (WIDTH,HEIGHT), interpolation=cv2.INTER_LINEAR)
x.append(good_image)
# if keyword in filename creat label
if "tool-hammer" in base:
y[row_number][0] = 1
y[row_number][1] = 0
row_number = row_number + 1
elif "screw" in base:
y[row_number][0] = 0
y[row_number][1] = 1
row_number = row_number + 1
return x, y
x,y = proc_images()
print(np.array(x).shape)
print(np.array(y).shape)
# Set it up as a dataframe
df = pd.DataFrame()
df["labels"]=y
df["images"]=x
np.savez("x_images_arrays", x)
np.savez("y", y)
Neural Network (feed forward)
我很确定一个cnn会预先形成一个床，但我不应该得到一个带有前馈架构（3个隐藏层）的床位预测吗？我还没有改变stddev .
# Imports
import tensorflow as tf
import numpy as np
import os
import pandas as pd
import matplotlib.pylab as plt
from sklearn.model_selection import train_test_split
# Load npz file containing image arrays
x_npz = np.load("x_images_arrays.npz")
x = x_npz['arr_0']
print(np.array(x).shape)
y_npz = np.load("y_labels.npz")
y = y_npz['arr_0']
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=42)
x_train = x_train[:, :]
x_test = x_test[:, :]
# Save Path
dir_path = "C:/Users/"
train_size = len(y_train)
test_size = len(y_test)
# Variables
epochs = 10
classes = 2
features = 90000
layer_nodes = [features, 100, 100, 100, classes]
stddev = 0.050
learning_rate = 5e-6
batch_size = 32
bias_wights_init = 0.050
epoch_errors = []
# TF Placeholders
x = tf.placeholder(dtype=tf.float32, shape=[None, features], name='x')
y = tf.placeholder(dtype=tf.float32, shape=[None, classes], name='y')
# Weights Matrices
W1 = tf.Variable(tf.truncated_normal([layer_nodes[0],
layer_nodes[1]],
stddev=stddev), name='W1')
W2 = tf.Variable(tf.truncated_normal([layer_nodes[1],
layer_nodes[2]],
stddev=stddev), name='W2')
W3 = tf.Variable(tf.truncated_normal([layer_nodes[2],
layer_nodes[3]],
stddev=stddev), name='W3')
W4 = tf.Variable(tf.truncated_normal([layer_nodes[3],
layer_nodes[4]],
stddev=stddev), name='W4')
# Biases Vectors
b1 = tf.Variable(tf.constant(bias_wights_init, shape=[layer_nodes[1]]), name='b1')
b2 = tf.Variable(tf.constant(bias_wights_init, shape=[layer_nodes[2]]), name='b2')
b3 = tf.Variable(tf.constant(bias_wights_init, shape=[layer_nodes[3]]), name='b3')
b4 = tf.Variable(tf.constant(bias_wights_init, shape=[layer_nodes[4]]), name='b4')
# Define the Deep Neural Network
def nn_model(x):
input_layer = {'weights': W1, 'biases': b1}
hidden_layer_1 = {'weights': W2, 'biases': b2}
hidden_layer_2 = {'weights': W3, 'biases': b3}
output_layer = {'weights': W4, 'biases': b4}
input_layer_sum = tf.add(tf.matmul(x, input_layer['weights']),
input_layer['biases'])
input_layer_sum = tf.nn.relu(input_layer_sum)
hidden_layer_1_sum = tf.add(tf.matmul(input_layer_sum, hidden_layer_1['weights']),
hidden_layer_1['biases'])
hidden_layer_1_sum = tf.nn.relu(hidden_layer_1_sum)
hidden_layer_2_sum = tf.add(tf.matmul(hidden_layer_1_sum, hidden_layer_2['weights']),
hidden_layer_2['biases'])
hidden_layer_2_sum = tf.nn.relu(hidden_layer_2_sum)
output_layer_sum = tf.add(tf.matmul(hidden_layer_2_sum, output_layer['weights']),
output_layer['biases'])
return output_layer_sum
training and testing
我尝试了不同的优化器和变量配置 . 我不得不重塑np数组 . 可能不是最好的地方......我不能经常达到59％以上的准确度 . 培训或测试功能是否有任何错误？
# Train the Neural Network
def nn_train(x):
pred = nn_model(x)
pred = tf.identity(pred)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
# TRAINING
for epoch in range(epochs):
epoch_loss = 0.0
batch_range_old = 0
for i in range(int(train_size / batch_size) + 1):
epoch_x, epoch_y, batch_range_old = load_next_batch(x_train, y_train, batch_range_old, batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
epoch_errors.append(epoch_loss)
print('epoch: ', epoch + 1, ' of ', epochs, ' with loss: ', epoch_loss)
# TESTING
correct_result = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accurancy = tf.reduce_mean(tf.cast(correct_result, 'float'))
acc_test = 0.0
for i in range(batch_size, test_size, batch_size):
acc_test = acc_test + accurancy.eval(
{x: np.reshape(x_test[i-batch_size:i], (32, -1)),
y: y_test[i-batch_size:i]})
acc_test = acc_test / len(range(batch_size, test_size, batch_size))
print('Accucary ', acc_test)
def load_next_batch(x, y, batch_range_x, batch_size):
batch_range_new = batch_range_x + batch_size
xa = x[batch_range_x:batch_range_new]
ya = y[batch_range_x:batch_range_new]
xa = np.reshape(xa, (32, -1))
return xa, ya, batch_range_x
if __name__ == "__main__":
nn_train(x)