Caffe : train network accuracy = 1 constant ! Accuracy issue - machine-learning

Question

Right now, I am train network with with 2 class data... but accuracy is constant 1 after first iteration !
Input data is grayscale images. both class images are randomly selected when HDF5Data creation.
Why is that happened ? What's wrong or where is mistake !
network.prototxt :
name: "brainMRI"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include: {
phase: TRAIN
}
hdf5_data_param {
source: "/home/shivangpatel/caffe/brainMRI1/train_file_location.txt"
batch_size: 10
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include: {
phase: TEST
}
hdf5_data_param {
source: "/home/shivangpatel/caffe/brainMRI1/test_file_location.txt"
batch_size: 10
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "softmax"
type: "Softmax"
bottom: "ip2"
top: "smip2"
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "smip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
Output :
I0217 17:41:07.912580 2913 net.cpp:270] This network produces output loss
I0217 17:41:07.912607 2913 net.cpp:283] Network initialization done.
I0217 17:41:07.912739 2913 solver.cpp:60] Solver scaffolding done.
I0217 17:41:07.912789 2913 caffe.cpp:212] Starting Optimization
I0217 17:41:07.912813 2913 solver.cpp:288] Solving brainMRI
I0217 17:41:07.912832 2913 solver.cpp:289] Learning Rate Policy: inv
I0217 17:41:07.920737 2913 solver.cpp:341] Iteration 0, Testing net (#0)
I0217 17:41:08.235076 2913 solver.cpp:409] Test net output #0: accuracy = 0.98
I0217 17:41:08.235194 2913 solver.cpp:409] Test net output #1: loss = 0.0560832 (* 1 = 0.0560832 loss)
I0217 17:41:35.831647 2913 solver.cpp:341] Iteration 100, Testing net (#0)
I0217 17:41:36.140849 2913 solver.cpp:409] Test net output #0: accuracy = 1
I0217 17:41:36.140949 2913 solver.cpp:409] Test net output #1: loss = 0.00757247 (* 1 = 0.00757247 loss)
I0217 17:42:05.465395 2913 solver.cpp:341] Iteration 200, Testing net (#0)
I0217 17:42:05.775877 2913 solver.cpp:409] Test net output #0: accuracy = 1
I0217 17:42:05.776000 2913 solver.cpp:409] Test net output #1: loss = 0.0144996 (* 1 = 0.0144996 loss)
.............
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Answer 1

Summarizing some information from the comments:
- You run test at intervals of test_interval:100 iterations.
- Each test interval goes over test_iter:5 * batch_size:10 = 50 samples.
- Your train and test sets seems to be very nit: all the negative samples (label=0) are grouped together before all the positive samples.
Consider your SGD iterative solver, you feed it batches of batch_size:10 during training. Your training set has 14,746 negative samples (that is 1474 batches) before any positive sample. So, for the first 1474 iterations your solver only "sees" negative examples and no positive ones.
What do you expect this solver will learn?
The problem
Your solver only sees negative examples, thus learns that no matter what the input is it should output "0". Your test set is also ordered in the same fashion, so testing only 50 samples at each test_interval, you only test on the negative examples in the test set resulting with a perfect accuracy of 1.
But as you noted, your net actually learned nothing.
Solution
I suppose you already guess what the solution should be by now. You need to shuffle your training set, and test your net on your entire test set.