{"id":15343,"date":"2025-02-11T09:58:23","date_gmt":"2025-02-11T00:58:23","guid":{"rendered":"https:\/\/www.emukk.com\/WP\/?p=15343"},"modified":"2025-02-12T08:55:51","modified_gmt":"2025-02-11T23:55:51","slug":"lecchip%ef%bc%88%e3%83%ac%e3%82%af%e3%83%81%e3%83%b3%e3%83%9e%e3%82%a4%e3%82%af%e3%83%ad%e3%82%a2%e3%83%ac%e3%82%a4%ef%bc%89%e3%81%ae%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e7%94%a8%e3%81%84%e3%81%a6deep","status":"publish","type":"post","link":"https:\/\/www.emukk.com\/WP\/lecchip%ef%bc%88%e3%83%ac%e3%82%af%e3%83%81%e3%83%b3%e3%83%9e%e3%82%a4%e3%82%af%e3%83%ad%e3%82%a2%e3%83%ac%e3%82%a4%ef%bc%89%e3%81%ae%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e7%94%a8%e3%81%84%e3%81%a6deep\/","title":{"rendered":"LecChip\uff08\u30ec\u30af\u30c1\u30f3\u30de\u30a4\u30af\u30ed\u30a2\u30ec\u30a4\uff09\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066Deep Learning\u3067\u7cd6\u9396\u306e\u69cb\u9020\u3084\u7d30\u80de\u7a2e\u306a\u3069\u3092\u6a5f\u68b0\u5b66\u7fd2\u3092\u884c\u308f\u305b\u308b\u6642\u306e\u74b0\u5883\u69cb\u7bc9\u3068Python Script\u4f8b"},"content":{"rendered":"

LecChip\uff08\u30ec\u30af\u30c1\u30f3\u30de\u30a4\u30af\u30ed\u30a2\u30ec\u30a4\uff09\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3001\u7cd6\u9396\u306e\u69cb\u9020\u3001\u7d30\u80de\u7a2e\u306a\u3069\u3092\u5224\u5225\u3055\u305b\u308b\u305f\u3081\u306b\u306f\u3001Deep Learning\u3092\u4f7f\u7528\u3057\u3066\u6ca2\u5c71\u306e\u30c7\u30fc\u30bf\u3092\u6a5f\u68b0\u5b66\u7fd2\u3055\u305b\u308b\u65b9\u6cd5\u304c\u6709\u52b9\u3067\u3059\u3002
\n\u3053\u308c\u3092\u884c\u3046\u70ba\u306b\u5fc5\u8981\u306a\u4e8b\u524d\u6e96\u5099\u306f\u3001\u4ee5\u4e0b\u3068\u306a\u308a\u307e\u3059\u3002
\nPython\uff08\u4ee5\u4e0b\u3067\u306fAnaconda3\u3092\u4f7f\u7528\uff09
\nTensorflow
\nKeras
\n\u5148\u305a\u306f\u3001\u3054\u81ea\u8eab\u306e\u30d1\u30bd\u30b3\u30f3\u306b\u3053\u308c\u3089\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u74b0\u5883\u69cb\u7bc9\u3092\u884c\u3063\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u3053\u306e\u3088\u3046\u306a\u4e8b\u524d\u6e96\u5099\u306e\u9762\u5012\u81ed\u3055\u3092\u7701\u304d\u3001Deep Learning\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u6210\u3092\u30de\u30a6\u30b9\u3067\u306e\u30af\u30ea\u30c3\u30af\u52d5\u4f5c\u3060\u3051\u3067\u51fa\u6765\u308b\u3088\u3046\u306b\u3057\u305f\u306e\u304c\u5f0a\u793e\u88fd\u54c1\u306e\u300cSA\/DL Easy<\/a>\u300d\u306b\u306a\u308a\u307e\u3059\u3002
\nSA\/DL Easy\u3092\u4f7f\u3046\u3068\u3001\u74b0\u5883\u69cb\u7bc9\u306e\u5fc5\u8981\u3082\u306a\u304f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306aScript\u3082\u66f8\u304b\u305a\u306b\u3001Deep Learning\u306e\u4e16\u754c\u304c\u305f\u3084\u3059\u304f\u4f7f\u3048\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n

\u4e0b\u8a18\u3092\u81ea\u8eab\u306e\u30d1\u30bd\u30b3\u30f3\u3067\u5b9f\u884c\u3055\u308c\u308b\u5834\u5408\u306b\u306f\u3001Python\u306eScript\u3092\u4fdd\u5b58\u3057\u305f\u30d5\u30a9\u30eb\u30c0\u30fc\u5185\u306bpath\u304c\u901a\u3063\u3066\u3044\u308b\u3053\u3068\u3001\u4fdd\u5b58\u3057\u305f\u5165\u529b\u30c7\u30fc\u30bf\u306epath\u3001\u5b66\u7fd2\u7d50\u679c\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u304c\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30d5\u30a9\u30eb\u30c0\u30fc\u306epath\u306a\u3069\u3092\u9593\u9055\u3048\u306a\u3044\u3088\u3046\u306b\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

———————————————————————————————
\n#\u3000LecChip\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u4f8b\u3048\u3070\u3001\u7cd6\u9396\u306e\u69cb\u9020\u3084\u7d30\u80de\u7a2e\u306e\u5224\u65ad\u3092\u5b66\u7fd2\u3055\u305b\u308b\u305f\u3081\u306eDeep Learning Python Script\u4f8b<\/p>\n

from __future__ import print_function
\nimport numpy as np
\nimport csv
\nimport pandas
\nfrom keras.datasets import mnist
\nfrom keras.models import Sequential
\nfrom keras.layers.core import Dense, Dropout, Activation
\nfrom keras.optimizers import RMSprop
\nfrom keras.utils import np_utils
\nfrom make_tensorboard import make_tensorboard<\/p>\n

np.random.seed(1671) # \u518d\u73fe\u6027\u3092\u826f\u304f\u3059\u308b\u305f\u3081\u306b<\/p>\n

# \u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u69cb\u6210\u3068\u5b66\u7fd2\u306e\u3055\u305b\u65b9
\nNB_EPOCH = 100 # \u4f55\u56de\u5b66\u7fd2\u3055\u305b\u308b\u304b\u3001\u9069\u5f53\u306b\u6c7a\u3081\u3066\u304f\u3060\u3055\u3044
\nBATCH_SIZE = 2\u3000\u3000# \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5e7e\u3064\u304b\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u306b\u5206\u3051\u308b
\nVERBOSE = 1
\nNB_CLASSES = 2 # \u6700\u7d42\u7684\u306a\u51fa\u529b\u6570
\nOPTIMIZER = RMSprop() # \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc
\nN_HIDDEN = 45\u3000\u3000# \u96a0\u308c\u5c64\u306e\u30ce\u30fc\u30c9\u6570\u3001\u3053\u3053\u3067\u306fLecChip\u306e\u30ec\u30af\u30c1\u30f3\u6570\u306b\u5408\u308f\u305b\u306645\u3068\u3057\u3066\u3044\u308b
\nVALIDATION_SPLIT = 0.2 # \u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u5185\u3001\u4f55\u5272\u3092\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3068\u3057\u3066\u4f7f\u3046\u304b
\nDROPOUT = 0.3
\nLECTINS = 45<\/p>\n

def drop(df):
\nreturn df[pandas.to_numeric(df.iloc[:, 2], errors=’coerce’).notnull()]<\/p>\n

#\u3000\u30c7\u30fc\u30bf\u306f\u6700\u5927\u5024\u3092\uff11\u3068\u3059\u308b\u3088\u3046\u306b\u898f\u683c\u5316\u3059\u308b
\ndef normalize_column(d):
\ndmax = np.max(d)
\ndmin = np.min(d)
\nreturn (np.log10(d + 1.0) – np.log10(dmin + 1.0)) \/ \\
\n(np.log10(dmax + 1.0) – np.log10(dmin + 1.0))<\/p>\n

def normalize(data):
\nreturn np.apply_along_axis(normalize_column, 0, data)<\/p>\n

# \u5165\u529b\u3059\u308b\u30c7\u30fc\u30bf\u306fCSV\u30d5\u30a1\u30a4\u30eb\u306e\u5f62\u5f0f\u3068\u3059\u308b
\ndf1 = drop(pandas.read_csv(r’c:\\Users\\Masao\\Anaconda3\\DL_scripts\\cell.csv’)).reset_index(drop=True)
\nX_train = normalize(df1.iloc[:, 2:].astype(np.float64))
\nfamily_column = df1.iloc[:, 1]
\nfamily_list = sorted(list(set(family_column)))
\nY_train = np.array([family_list.index(f) for f in family_column])<\/p>\n

df2 = drop(pandas.read_csv(r’c:\\Users\\Masao\\Anaconda3\\DL_scripts\\cell_test.csv’)).reset_index(drop=True)
\nX_test = normalize(df2.iloc[:, 2:].astype(np.float64))
\nfamilyt_column = df2.iloc[:, 1]
\nfamilyt_list = sorted(list(set(familyt_column)))
\nY_test = np.array([familyt_list.index(f) for f in familyt_column])<\/p>\n

print(X_train.shape[0], ‘train samples’)
\nprint(X_test.shape[0], ‘test samples’)<\/p>\n

# convert class vectors to binary class matrices
\nY_train = np_utils.to_categorical(Y_train, NB_CLASSES)
\nY_test = np_utils.to_categorical(Y_test, NB_CLASSES)<\/p>\n

print(X_train)
\nprint(Y_train)
\nprint(X_test)
\nprint(Y_test)<\/p>\n

# \u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5177\u4f53\u7684\u306a\u69cb\u6210\u4f8b
\n# \u96a0\u308c\u5c64\u306f\uff12\u5c64
\n# \u5165\u529b\u306fLecChip\u306e\u30c7\u30fc\u30bf\uff0845\u30ec\u30af\u30c1\u30f3\u3092\u4f7f\u7528\uff09
\n# \u6700\u7d42\u5c64\u306fsoftmax\u3067\u6d3b\u6027\u5316<\/p>\n

model = Sequential()
\nmodel.add(Dense(N_HIDDEN, input_shape=(LECTINS,)))
\nmodel.add(Activation(‘relu’))
\nmodel.add(Dropout(DROPOUT))
\nmodel.add(Dense(N_HIDDEN))
\nmodel.add(Activation(‘relu’))
\nmodel.add(Dropout(DROPOUT))
\nmodel.add(Dense(NB_CLASSES))
\nmodel.add(Activation(‘softmax’))
\nmodel.summary()<\/p>\n

#\u3000\u5b66\u7fd2\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u72b6\u6cc1\u3092Tensorboard\u306b\u51fa\u529b\u3057\u3066\u53ef\u8996\u5316\u3055\u305b\u308b
\ncallbacks = [make_tensorboard(set_dir_name=’Glycan_Profile’)]<\/p>\n

model.compile(loss=’categorical_crossentropy’,
\noptimizer=OPTIMIZER,
\nmetrics=[‘accuracy’])<\/p>\n

model.fit(X_train, Y_train,
\nbatch_size=BATCH_SIZE, epochs=NB_EPOCH,
\ncallbacks=callbacks,
\nverbose=VERBOSE, validation_split=VALIDATION_SPLIT)<\/p>\n

score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
\nprint(“\\nTest score:”, score[0])
\nprint(‘Test accuracy:’, score[1])<\/p>\n

————————————————————————————
\n# Tensorboard\u3092\u4f7f\u3046\u305f\u3081\u306ePython Script<\/p>\n

# -*- coding: utf-8 -*-
\nfrom __future__ import absolute_import
\nfrom __future__ import unicode_literals
\nfrom time import gmtime, strftime
\nfrom keras.callbacks import TensorBoard
\nimport os<\/p>\n

def make_tensorboard(set_dir_name=”):
\nymdt = strftime(“%a_%d_%b_%Y_%H_%M_%S”, gmtime())
\ndirectory_name = ymdt
\nlog_dir = set_dir_name + ‘_’ + directory_name
\nos.mkdir(log_dir)
\ntensorboard = TensorBoard(log_dir=log_dir, write_graph=True, )
\nreturn tensorboard<\/p>\n

————————————————————————————
\nTensorboard\u3092\u7528\u3044\u3066\u53ef\u8996\u5316\u3055\u305b\u308b\u306b\u306f\u3001make_tensorboard.py\u3092\u8d70\u3089\u305b\u305f\u3046\u3048\u3067\u3001
\n$ tensorboard –logdir=.\/Glycan_Profile_Mon_10_Feb_2025_23_06_26\uff08.\/\u30c7\u30fc\u30bf\u304c\u8a18\u9332\u3055\u308c\u3066\u3044\u308b\u30d5\u30a9\u30eb\u30c0\u30fc\uff09\u3092\u8d70\u3089\u305b
\nhttp:\/\/localhost:6006\/\u306b\u30a2\u30af\u30bb\u30b9\u3057\u307e\u3059\u3002<\/p>\n

(base) PS C:\\Users\\masao\\Anaconda3\\DL_Scripts> python make_tensorboard.py
\nUsing TensorFlow backend.
\n(base) PS C:\\Users\\masao\\Anaconda3\\DL_Scripts> tensorboard –logdir=.\/Glycan_Profile_Mon_10_Feb_2025_23_06_26
\nServing TensorBoard on localhost; to expose to the network, use a proxy or pass –bind_all
\nTensorBoard 2.0.2 at http:\/\/localhost:6006\/ (Press CTRL+C to quit)<\/p>\n

\"\"<\/p>\n

—————————————————————————————————-
\nLecChip\u306e\u30c7\u30fc\u30bf\u306f\u3001\u4e0b\u8a18\u306e\u3088\u3046\u306aCSV\u5f62\u5f0f\u3068\u3057\u307e\u3059\u3002
\n\u5de6\u7aef\u304b\u3089\u3001\u30b5\u30f3\u30d7\u30eb\u540d\u3001\u30d5\u30a1\u30df\u30ea\u30fc\u540d\uff08\u6559\u5e2b\u30c7\u30fc\u30bf\u3068\u306a\u308a\u307e\u3059\uff09\u3001\u5404\u7a2e\u30ec\u30af\u30c1\u30f3\u306e\u6570\u5024\u304c\u4e26\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n

\"\"<\/p>\n

—————————————————————————————————-
\n\u5b66\u7fd2\u304c\u7d42\u308f\u3063\u305f\u3089\u3001\u30e2\u30c7\u30eb\u306e\u4fdd\u5b58\u3092\u3057\u3066\u304a\u304f\u3079\u304d\u3067\u3057\u3087\u3046\u3002
\n\u30e2\u30c7\u30eb\u304c\u4fdd\u5b58\u3057\u3066\u3042\u308c\u3070\u5fa9\u5143\u3082\u3067\u304d\u307e\u3059\u3057\u3001\u672a\u77e5\u30c7\u30fc\u30bf\u3092\u4e0e\u3048\u3066\u4e88\u6e2c\u3055\u305b\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002
\n\u3053\u306e\u8fba\u308a\u306eScript\u306f\u5225\u9014\u66f8\u304f\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

LecChip\uff08\u30ec\u30af\u30c1\u30f3\u30de\u30a4\u30af\u30ed\u30a2\u30ec\u30a4\uff09\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3001\u7cd6\u9396\u306e\u69cb\u9020\u3001\u7d30\u80de\u7a2e\u306a\u3069\u3092\u5224\u5225\u3055\u305b\u308b\u305f\u3081\u306b\u306f\u3001Deep Learning\u3092\u4f7f\u7528\u3057\u3066\u6ca2\u5c71\u306e\u30c7\u30fc\u30bf\u3092\u6a5f\u68b0\u5b66\u7fd2\u3055\u305b\u308b\u65b9\u6cd5\u304c\u6709\u52b9\u3067\u3059\u3002 \u3053\u308c\u3092\u884c\u3046\u70ba\u306b\u5fc5\u8981\u306a\u4e8b\u524d\u6e96\u5099\u306f\u3001\u4ee5<\/p><\/div>\n

\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42],"tags":[],"class_list":["post-15343","post","type-post","status-publish","format-standard","hentry","category-technology-ja"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/posts\/15343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/comments?post=15343"}],"version-history":[{"count":6,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/posts\/15343\/revisions"}],"predecessor-version":[{"id":15363,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/posts\/15343\/revisions\/15363"}],"wp:attachment":[{"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/media?parent=15343"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/categories?post=15343"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.emukk.com\/WP\/wp-json\/wp\/v2\/tags?post=15343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}