TensorFlow | ADDA > 線形方程式の初期値用データの学習 (TFRecords版) > 学習コード:v0.4 | 結果確認コード:v0.3 > 再現率はまだ悪い

```txt:動作環境
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 16.04 LTS desktop amd64
TensorFlow v1.1.0
cuDNN v5.1 for Linux
CUDA v8.0
Python 3.5.2
IPython 6.0.0 -- An enhanced Interactive Python.
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)
```

学習コードv0.1 http://qiita.com/7of9/items/5819f36e78cc4290614e

http://qiita.com/7of9/items/ba8197ac83619c3cc749
の続き。

### 変更点

- 学習コード v0.4
    - ステップを90,000から270,000に増やしてみた
- 結果確認コード v0.3
    - TARGET_IDX.EXRなどのインデックス間違い修正
    - 対象とするZ, mr, mi条件確認のif文の間違い修正
        - 付随して、処理時間が4分から20秒に改善した
    - plt.imshow()にvmin, vmaxをつけてみた
        - カラーバーの表示範囲の統一

## 学習コードv0.4

```learn_mr_mi_170722.py
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim

"""
v0.4 Jul. 23, 2017
  - increase step from [90000] to [270000]
v0.3 Jul. 22, 2017
  - output model variables
v0.2 Jul. 22, 2017
  - increase step from [30000] to [90000]
  - change [capacity]
v0.1 Jul. 22, 2017
  - increase network structure from [7,7,7] to [100,100,100]
  - increase dimension of [input_ph], [output_ph]
  - alter read_and_decode() to treat 5 input-, 6 output- nodes
  - alter [IN_FILE] to the symbolic linked file

  :reference: [learnExr_170504.py] to expand dimensions to [input:3,output:6]

=== branched from [learn_sineCurve_170708.py] ===
v0.6 Jul. 09, 2017
  - modify for PEP8
  - print prediction after learning
v0.5 Jul. 09, 2017
  - fix bug > [Attempting to use uninitialized value hidden/hidden_1/weights]
v0.4 Jul. 09, 2017
  - fix bug > stops only for one epoch
    + set [num_epochs=None] for string_input_producer()
  - change parameters for shuffle_batch()
  - implement training
v0.3 Jul. 09, 2017
  - fix warning > use tf.local_variables_initializer() instead of
       initialize_local_variables()
  - fix warning > use tf.global_variables_initializer() instead of
       initialize_all_variables()
v0.2 Jul. 08, 2017
  - fix bug > OutOfRangeError (current size 0)
    + use [tf.initialize_local_variables()]
v0.1 Jul. 08, 2017
  - only read [.tfrecords]
    + add inputs_xy()
    + add read_and_decode()
"""

# codingrule: PEP8

IN_FILE = 'LN-IntField-Y_170722.tfrecords'


def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        features={
           'xpos_raw': tf.FixedLenFeature([], tf.string),
           'ypos_raw': tf.FixedLenFeature([], tf.string),
           'zpos_raw': tf.FixedLenFeature([], tf.string),
           'mr_raw': tf.FixedLenFeature([], tf.string),
           'mi_raw': tf.FixedLenFeature([], tf.string),
           'exr_raw': tf.FixedLenFeature([], tf.string),
           'exi_raw': tf.FixedLenFeature([], tf.string),
           'eyr_raw': tf.FixedLenFeature([], tf.string),
           'eyi_raw': tf.FixedLenFeature([], tf.string),
           'ezr_raw': tf.FixedLenFeature([], tf.string),
           'ezi_raw': tf.FixedLenFeature([], tf.string),
        })
    xpos_raw = tf.decode_raw(features['xpos_raw'], tf.float32)
    ypos_raw = tf.decode_raw(features['ypos_raw'], tf.float32)
    zpos_raw = tf.decode_raw(features['zpos_raw'], tf.float32)
    mr_raw = tf.decode_raw(features['mr_raw'], tf.float32)
    mi_raw = tf.decode_raw(features['mi_raw'], tf.float32)
    exr_raw = tf.decode_raw(features['exr_raw'], tf.float32)
    exi_raw = tf.decode_raw(features['exi_raw'], tf.float32)
    eyr_raw = tf.decode_raw(features['eyr_raw'], tf.float32)
    eyi_raw = tf.decode_raw(features['eyi_raw'], tf.float32)
    ezr_raw = tf.decode_raw(features['ezr_raw'], tf.float32)
    ezi_raw = tf.decode_raw(features['ezi_raw'], tf.float32)

    xpos_org = tf.reshape(xpos_raw, [1])
    ypos_org = tf.reshape(ypos_raw, [1])
    zpos_org = tf.reshape(zpos_raw, [1])
    mr_org = tf.reshape(mr_raw, [1])
    mi_org = tf.reshape(mi_raw, [1])
    exr_org = tf.reshape(exr_raw, [1])
    exi_org = tf.reshape(exi_raw, [1])
    eyr_org = tf.reshape(eyr_raw, [1])
    eyi_org = tf.reshape(eyi_raw, [1])
    ezr_org = tf.reshape(ezr_raw, [1])
    ezi_org = tf.reshape(ezi_raw, [1])
    # input
    wrk = [xpos_org[0], ypos_org[0], zpos_org[0], mr_org[0], mi_org[0]]
    inputs = tf.stack(wrk)
    # output
    wrk = [exr_org[0], exi_org[0],
           eyr_org[0], eyi_org[0],
           ezr_org[0], ezi_org[0]]
    outputs = tf.stack(wrk)
    return inputs, outputs


def inputs_xy():
    filename = IN_FILE
    filequeue = tf.train.string_input_producer(
        [filename], num_epochs=None)

    in_org, out_org = read_and_decode(filequeue)
    return in_org, out_org

in_orgs, out_orgs = inputs_xy()
batch_size = 4  # [4]
# Ref: cifar10_input.py
min_fraction_of_examples_in_queue = 0.2  # 0.4
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 223872  # 223872 or 9328
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
                         min_fraction_of_examples_in_queue)
cpcty = min_queue_examples + 3 * batch_size
in_batch, out_batch = tf.train.shuffle_batch([in_orgs, out_orgs],
                                             batch_size,
                                             capacity=cpcty,
                                             min_after_dequeue=batch_size)

input_ph = tf.placeholder("float", [None, 5])
output_ph = tf.placeholder("float", [None, 6])  # [6]

# network
hiddens = slim.stack(input_ph, slim.fully_connected, [100, 100, 100],
                     activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 6,
                                  activation_fn=None, scope="output")
loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)

train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.001))

init_op = [tf.global_variables_initializer(), tf.local_variables_initializer()]

with tf.Session() as sess:
    sess.run(init_op)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    try:
        for idx in range(270000):  # 90000
            inpbt, outbt = sess.run([in_batch, out_batch])
            _, t_loss = sess.run([train_op, loss],
                                 feed_dict={input_ph: inpbt, output_ph: outbt})
            if (idx + 1) % 100 == 0:
                print("%d,%f" % (idx+1, t_loss))
    finally:
        coord.request_stop()

    # output the model
    model_variables = slim.get_model_variables()
    res = sess.run(model_variables)
    np.save('model_variables_170722.npy', res)

    coord.join(threads)

```

### 結果確認コード v0.3

```py:show_original_170722.ipynb
import numpy as np
import tensorflow as tf
import math
import sys
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import datetime 

"""
v0.3 Jul. 23, 2017
    - add [COLOR_RANGE] and (vmin, vmax) for plt.imshow()
    - fix bug > if abs() statement is the other way around
    - fix bug > TARGET_IDX.EXR..EXI is wrong
v0.2 Jul. 22, 2017
    - update calc_conv()
v0.1 Jul. 22, 2017
    - change for input incuding [mr],[mi]
=== branched from [display_TFRecords_IntField_170709.ipynb: v0.1] ===
"""

# on
#   Ubuntu 16.04 LTS
#   TensorFlow v1.1
#   Python 3.5.2
#   IPython 6.0.0 -- An enhanced Interactive Python.


def calc_sigmoid(x):
    return 1.0 / (1.0 + math.exp(-x))


def calc_conv(src, weight, bias, applyActFnc):
    wgt = weight.shape
#   print wgt # debug
    #conv = list(range(bias.size))
    conv = [0.0] * bias.size

    # weight
    #for idx1 in range(wgt[0]):
    #    for idx2 in range(wgt[1]):
    #        conv[idx2] = conv[idx2] + src[idx1] * weight[idx1, idx2]
    for   idx2 in range(wgt[1]):
        tmp_vec = weight[:,idx2] * src[:]
        conv[idx2] = tmp_vec.sum()

    # bias
    for idx2 in range(wgt[1]):
        conv[idx2] = conv[idx2] + bias[idx2]
    # activation function
    if applyActFnc:
        for idx2 in range(wgt[1]):
            conv[idx2] = calc_sigmoid(conv[idx2])
    return conv  # return list


def get_feature_float32(example, feature_name):
    wrk_raw = (example.features.feature[feature_name]
               .bytes_list
               .value[0])
    wrk_1d = np.fromstring(wrk_raw, dtype=np.float32)
    wrk_org = wrk_1d.reshape([1, -1])
    return wrk_org


def get_group_features(example):
    xpos_org = get_feature_float32(example, 'xpos_raw')
    ypos_org = get_feature_float32(example, 'ypos_raw')
    zpos_org = get_feature_float32(example, 'zpos_raw')
    mr_org = get_feature_float32(example, 'mr_raw')
    mi_org = get_feature_float32(example, 'mi_raw')
    exr_org = get_feature_float32(example, 'exr_raw')
    exi_org = get_feature_float32(example, 'exi_raw')
    eyr_org = get_feature_float32(example, 'eyr_raw')
    eyi_org = get_feature_float32(example, 'eyi_raw')
    ezr_org = get_feature_float32(example, 'ezr_raw')
    ezi_org = get_feature_float32(example, 'ezi_raw')

    pos = xpos_org[0], ypos_org[0], zpos_org[0], mr_org[0], mi_org[0]
    ex = exr_org[0], exi_org[0]
    ey = eyr_org[0], eyi_org[0]
    ez = ezr_org[0], ezi_org[0]
    return pos + ex + ey + ez


class TARGET_IDX:
    EXR, EXI = 5, 6  # real and imaginary part of Ex
    EYR, EYI = 7, 8
    EZR, EZI = 9, 10

# parameters
SIZE_MAP_X = 30  # size of the image
SIZE_MAP_Y = 30
ZOOM_X = 15.0  #
ZOOM_Y = 15.0
SHIFT_X = 15.0  # to shift the center position
SHIFT_Y = 15.0
orgmap = [[0.0 for yi in range(SIZE_MAP_Y)] for xi in range(SIZE_MAP_X)]
outmap = [[0.0 for yi in range(SIZE_MAP_Y)] for xi in range(SIZE_MAP_X)]
 
INP_FILE = 'LN-IntField-Y_170722.tfrecords'
NETWORK_FILE = 'model_variables_170722.npy'

pickUpZvalue = 0.23621  # arbitrary selected
pickUpRealm = 1.33  # 1.45  # arbitrary selected
pickUpImagm = 0.0001  # arbitrary selected 

print(datetime.datetime.now())

atargetIdx = TARGET_IDX.EXR  # [set this]

model_var = np.load(NETWORK_FILE)

record_iterator = tf.python_io.tf_record_iterator(path=INP_FILE)
for record in record_iterator:
    example = tf.train.Example()
    example.ParseFromString(record)

    tpl = get_group_features(example)
    xpos_val, ypos_val, zpos_val = tpl[0:3]
    mr_val, mi_val = tpl[3:5]
    ax, ay, az = *xpos_val, *ypos_val, *zpos_val
    amr, ami = *mr_val, *mi_val
    aTarget = tpl[atargetIdx]
    
    if (abs(az - pickUpZvalue) > 1e-7 or
        abs(amr - pickUpRealm) > 1e-7 or
        abs(ami - pickUpImagm) > 1e-7):
        continue

    xidx = (SIZE_MAP_X * ax / ZOOM_X + SHIFT_X).astype(int)
    yidx = (SIZE_MAP_Y * ay / ZOOM_Y + SHIFT_Y).astype(int)

    if (xidx < 0 or xidx >= SIZE_MAP_X or
        yidx < 0 or yidx >= SIZE_MAP_Y):
        continue
        
    # input layer (5 nodes)
    inlist = (ax, ay, az, amr, ami) 
    # hidden layer 1
    outdata = calc_conv(inlist, model_var[0], model_var[1], applyActFnc=True)
    # hidden layer 2
    outdata = calc_conv(outdata, model_var[2], model_var[3], applyActFnc=True)
    # hidden layer 3
    outdata = calc_conv(outdata, model_var[4], model_var[5], applyActFnc=True)
    # output layer
    outdata = calc_conv(outdata, model_var[6], model_var[7], applyActFnc=False)
    
    orgmap[xidx][yidx] = aTarget  # overwrite
    outmap[xidx][yidx] = outdata[atargetIdx - TARGET_IDX.EXR]
    #outmap[xidx][yidx] = 0.0  # dummy

# draw map
COLOR_RANGE = 0.3
#   original
plt.subplot(121)
figmap = np.reshape(np.array(orgmap), (SIZE_MAP_X, SIZE_MAP_Y))
plt.imshow(figmap, extent=(0, SIZE_MAP_X, 0, SIZE_MAP_Y), cmap=cm.jet, vmin=-COLOR_RANGE, vmax=COLOR_RANGE)
plt.colorbar()
#   learnt
plt.subplot(122)
figmap = np.reshape(np.array(outmap), (SIZE_MAP_X, SIZE_MAP_Y))
plt.imshow(figmap, extent=(0, SIZE_MAP_X, 0, SIZE_MAP_Y), cmap=cm.jet, vmin=-COLOR_RANGE, vmax=COLOR_RANGE)
plt.colorbar()

plt.show()

print(datetime.datetime.now())

```

### lossの減少

減少しているが0.02程度はある。

![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/de9f189d-663b-3749-3d0a-95d6dafcc4c9.png)

### 結果確認

EXRの比較 (左が元, 右が学習結果)
![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/5e7db918-a819-9c6a-67af-5b1752f14cd5.png)

EXIの比較
![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/dbd826e0-1dd1-65d2-ba41-976fec35e72d.png)

他の値についても再現率は高くない。

## mr, mi固定

mr=1.33, mi=0.0001iのデータだけに対して学習をしてみた。

lossの減少は良くなった。
![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/b19e311e-f419-e881-4e85-ab4a2f869d16.png)

EXRの再現
![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/360d3ae8-8cfd-e9a3-478e-19b3f63926a5.png)

EXIの再現
![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/72953b0d-3d82-b315-79cd-2a3190c0e7e0.png)

再現率はまだ悪い。