Python ValueError:所有输入数组的维数必须相同-对于Tensorflow Manual MSINT
我正在尝试使用我在tensor flow中制作的随机梯度下降代码,并训练25112张类似于MINST数据集的图像(这些文件看起来非常像它)。我很抱歉,如果这是一个简单的问题,但我不知道如何继续。谢谢大家! 我遇到了以下错误: “ValueError:所有输入数组的维数必须相同” 在这一行代码中: x=np.c.[np.ones(n),图像张量2]#第75行 我无法确定为什么这不起作用-我想这与我如何读取图像文件有关,但我不能确定。这是我的密码Python ValueError:所有输入数组的维数必须相同-对于Tensorflow Manual MSINT,python,numpy,tensorflow,Python,Numpy,Tensorflow,我正在尝试使用我在tensor flow中制作的随机梯度下降代码,并训练25112张类似于MINST数据集的图像(这些文件看起来非常像它)。我很抱歉,如果这是一个简单的问题,但我不知道如何继续。谢谢大家! 我遇到了以下错误: “ValueError:所有输入数组的维数必须相同” 在这一行代码中: x=np.c.[np.ones(n),图像张量2]#第75行 我无法确定为什么这不起作用-我想这与我如何读取图像文件有关,但我不能确定。这是我的密码 import numpy as np import
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import argparse
#load the images in order
vector = [] #initialize the vector
filenames = tf.train.match_filenames_once("train_data/*.jpg")
filename_queue = tf.train.string_input_producer(filenames)
image_reader = tf.WholeFileReader()
_, image_file = image_reader.read(filename_queue)
image_orig = tf.image.decode_jpeg(image_file)
image = tf.image.resize_images(image_orig, [28, 28])
image.set_shape((28, 28, 3))
images = tf.image.decode_jpeg(image_file)
with tf.Session() as sess:
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
image_tensor = sess.run([images])
#print(image_tensor)
#coord.request_stop()
#coord.join(threads)
image_tensor2 = np.array(image_tensor)
n_samples = image_tensor2.shape[0]
lossHistory=[]
ap = argparse.ArgumentParser()
ap.add_argument("-b", "--batch-size", type = int, default =32, help = "size of SGD mini-batches")
args = vars(ap.parse_args())
# Create the model
x = tf.placeholder(tf.float32, [None, 784]) #784=28*28
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
y_ = tf.placeholder(tf.float32, [25112, 10])
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
def next_batch(x, batchSize):
for i in np.arange(0, x.shape[0], batchSize):
yield (x[i:i + batchSize])
def gradient_descent_2(alpha, x, y, numIterations):
m,n = (784, 25112) # number of samples
theta = np.ones(n)
theta.fill(0.01)
x_transpose = x.transpose()
losshistory=[]
count = 0
batchX = 50
for (batchX) in next_batch(x, args["batch_size"]):
for iter in range(0, numIterations):
hypothesis = np.dot(x, theta)
loss = hypothesis - y
J = np.sum(loss ** 2) / (2 * m) # cost
lossHistory.append(J)
print( "iter %s | J: %.3f" % (iter, J))
gradient = np.dot(x_transpose, loss) / m
theta = theta - alpha * gradient
return theta
if __name__ == '__main__':
m, n = (784, 25112)
x = np.c_[ np.ones(n), image_tensor2] # insert column
alpha = 0.001 # learning rate
theta = gradient_descent_2(alpha, image_tensor2, y_, 50)
fig = plt.figure()
print(theta)
我的直觉是图像张量是三维数组,而np.ones(n)是创建一维数组 但你的目标是插入一个偏见栏(我猜),一个快速的方法是:
b = np.ones((n + n+1))
x = b[:, 1:] = image_tensor2
示例
c =
array([[7, 5, 6, 8, 7],
[5, 8, 7, 9, 7],
[9, 5, 6, 5, 8]])
b = np.ones((3, 6))
d = b[:, 1:] =c
d =
array([[ 1., 7., 5., 6., 8., 7.],
[ 1., 5., 8., 7., 9., 7.],
[ 1., 9., 5., 6., 5., 8.]])
好。图像张量2(图像张量2.形状)的尺寸是多少?