Google ColabでKaggle! 【STEP2:データ前処理とか】
前提
まず前提として言語はpythonでpandasを使用しています．
データのダウンロード
これは参加するコンペのdatasetダウンロードするだけです．
#ダウンロード可能なコンペ一覧 !kaggle competitions list #ダウンロード !kaggle competitions download -c titanic
データの把握
どんなデータなのかを把握することがまず第一歩です．
import pandas as pd import numpy as np train = pd.read_csv("train.csv") test = pd.read_csv("test.csv") print(train.head())
次に欠損値（null）が何個あるのか，どの項目にあるのか見てみます．
## 値がnullの項目数を数える train.isnull().sum() ############### PassengerId 0 Survived 0 Pclass 0 Name 0 Sex 0 Age 177 SibSp 0 Parch 0 Ticket 0 Fare 0 Cabin 687 Embarked 2 dtype: int64 ################
以上からデータセット内の全体を把握します．
次回は下記の内容です
データの前処理（必要な場合）
提供されているデータを取り扱うために前処理を行う必要があります。 前処理は沢山種類がありますがここでは2種類を紹介しておきます。
欠損データの補完
この欠損値をどう処理することがベターなのかを議論することはとても重要ですが簡単な方法だけとりあえずまとめます．
Ageの補完
今回のAgeはmean(平均値)とmedian(中央値)とmode(最頻値)の3つの例を示します．
#case of mean train["Age"] = train["Age"].fillna(train["Age"].mean()) #case of median train["Age"] = train["Age"].fillna(train["Age"].median()) #case of mode train["Age"] = train["Age"].fillna(train["Age"].mode())
文字列データを数値データ形式へ変換
#
複数GPU所持しているPCでKeras内に使用GPUの指定方法
使用方法
import tensorflow as tf from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto( gpu_options=tf.GPUOptions( visible_device_list="2", # specify GPU number allow_growth=True ) ) set_session(tf.Session(config=config))
(https://github.com/fchollet/keras/issues/1538)
基本的にはバックエンドとして動作するTensorflowの設定を変更します．keras.backend.tensorflow_backend.set_sessionを用います． 注意点としては，コードの内部でtf.Sessionを取得しているようなケースでは，先頭に上記を記述しても意味がありません（とくに自分で書いていないコードを走らせる場合）．
参考
pythonでの複素数の取り扱いと極座標変換し極座標グラフへプロットしてみた
pythonでの複素数の取り扱い
研究の中でpythonで複素数を使うことが多々あるためまとめておきます． pythonでは複素数は簡単に取り扱うことが可能である．
虚数単位をjで表す．（Not i)
c = 2 + 5j
以上です
複素数の極座標変換
cmath.polar()を使うと(絶対値, 偏角)のタプルでまとめて取得が可能です．
c = 2 + 1j print(cmath.polar(c))
極座標変換したものを極座標グラフへ
import numpy as np import matplotlib.pyplot as plt import cmath c =11618.669889921717+7.8985760292917565e-19j print(cmath.polar(c)) plt.polar(cmath.polar(c)) # 極座標グラフのプロット plt.show()
参考
Google ColabでKaggle! 【STEP1: Kaggle APIの設定とか】
Google Colabを起動
自分のGoogleアカウントからGoogle Colabを起動して開いて下さい
Google Drive＞新規＞その他＞Colaboratory
kaggle APIのインストール
!pip install kaggle
Kaggle API Keyをダウンロードする
Kaggleを開いて自分のアカウントページを開く
アドレスのサンプル
https://www.kaggle.com/
Create New API Token をクリック kaggle.json をダウンロード
Google Colabへkaggle.jsonをアップロード
from google.colab import files files.upload()
アップロード完了後、kaggle.jsonを移動する
!mkdir -p ~/.kaggle !cp kaggle.json ~/.kaggle/
参考
https://stackoverflow.com/questions/49310470/using-kaggle-datasets-into-google-colab
https://colab.research.google.com/drive/1DofKEdQYaXmDWBzuResXWWvxhLgDeVyl?pli=1&authuser=2
以上 次回は実際の推測や提出までです
How to use numpy.squeeze() squeeze()の使い方
squeeze()は，配列の中に次元数1があるならばその次元を削除する． 例えば (28, 28, 1)の場合は(28, 28)となる．
画像処理で(28, 28, 1)のチャネル部分を削減してnumpy.imshow() などの関数を使って，グレースケール画像を表示しようした際に便利
KerasでCNNの中間層の可視化（特徴マップ）や重みの可視化まとめ
kerasで中間層の出力を取得
kerasでCNNの中間層を取得する方法は２種類存在する．
ケース１
from keras.models import Model intermediante_layer_model = Model(inputs=model.input, outputs=model.get_layer("fc2").output) y = intermediante_layer_model.predict(X) print(y.shape)
ケース２
from keras import backend as K get_layer_output = K.function([model.layers[0].input],[model.layers[21].output]) y = get_layer_output([X,0])[0] print(y.shape)
中間層の重みを可視化
中間層の重みを可視化するためには上記の中間層の取得のときに使ったコードを利用する．
weights = resnet.get_layer("conv1").get_weights()[0]
from keras.applications import ResNet50 import matplotlib.pyplot as plt import numpy as np from PIL import Image <200b> resnet = ResNet50() resnet.summary() <200b> weights = resnet.get_layer("conv1").get_weights()[0] <200b> <200b> weights.shape print("shape",weights.shape) <200b> w = weights[:, :, ::-1, 0].copy() m = w.min() M = w.max() w = (w-m)/(M-m) plt.imshow(w) <200b> result = Image.new("RGB", (7*8+(8-1), 7*8+(8-1))) for i in range(64): w= weights[:, :, ::-1, i].copy() M = w.max() m = w.min() w = (w-m)/(M-m) w *= 255 img = Image.fromarray(w.astype("uint8"), mode="RGB") result.paste(img, (7*(i//8) + (i//8), 7*(i%8)+(i%8))) plt.imshow(result)
激安ラジコン（RC）の自動運転化計画※RCをEV3に変更しました
目的：ラジコンの自動運転をすること
使ったもの
ハード
システムの概要
今回用いたコースはこちら
今回は言語をpython限定．
行動の分類を線の数を考慮したクラス分類問題とした
画像転送部分（動画の配信）
- MJPG-streamerを使ってwebカメラから取得した画像をストリーミングを行う．
*設定
fps:5
width:640
height:480
*コマンド
./mjpg_streamer -i "./input_uvc.so -f 10 -r 320x240 -d /dev/video0 -y -n" -o "./output_http.so -w ./www -p 8080"
モータ制御部分
- EV3の二つのモータの制御を行う．サーバ（PC）側の分類結果からそれに対応する制御信号をMQTTにより受信し，モータの駆動させる． *プログラム
学習・検証部分
- KerasによりCNN部分の実装を行う． *プログラム
#coding:utf-8 import os from keras.applications.vgg16 import VGG16 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential, Model from keras.layers import Input, Activation, Dropout, Flatten, Dense from keras.preprocessing.image import ImageDataGenerator from keras import optimizers import numpy as np classes = ['foward_1', 'foward_2', 'left_1', 'left_2', 'right_1', 'right_2', 'other'] batch_size = 32 nb_classes = len(classes) img_rows, img_cols = 150, 150 channels = 3 train_data_dir = 'data/train' validation_data_dir = 'data/test' nb_train_samples = 1699 nb_val_samples = 447 nb_epoch = 100 result_dir = 'results' if not os.path.exists(result_dir): os.mkdir(result_dir) if __name__ == '__main__': # VGG16モデルと学習済み重みをロード # Fully-connected層（FC）はいらないのでinclude_top=False） input_tensor = Input(shape=(img_rows, img_cols, 3)) vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor) # vgg16.summary() # FC層を構築 # Flattenへの入力指定はバッチ数を除く top_model = Sequential() top_model.add(Flatten(input_shape=vgg16.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(nb_classes, activation='softmax')) # 学習済みのFC層の重みをロード # top_model.load_weights(os.path.join(result_dir, 'bottleneck_fc_model.h5')) # VGG16とFCを接続 model = Model(input=vgg16.input, output=top_model(vgg16.output)) # 最後のconv層の直前までの層をfreeze for layer in model.layers[:15]: layer.trainable = False # Fine-tuningのときはSGDの方がよい？ model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1e-4, momentum=0.9), metrics=['accuracy']) # train_datagen = ImageDataGenerator(featurewise_center=False, # samplewise_center=False, # featurewise_std_normalization=False, # samplewise_std_normalization=False, # zca_whitening=False, # rotation_range=0.2, # width_shift_range=0.2, # height_shift_range=0.2, # shear_range=0.2, # zoom_range=0.2, # channel_shift_range=0.1, # fill_mode='nearest', # cval=0., # horizontal_flip=True, # vertical_flip=True, # rescale=None) train_datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0.1, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, channel_shift_range=0.1, fill_mode='nearest', rescale=None) test_datagen = ImageDataGenerator() train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_rows, img_cols), color_mode='rgb', classes=classes, class_mode='categorical', batch_size=batch_size, shuffle=True) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_rows, img_cols), color_mode='rgb', classes=classes, class_mode='categorical', batch_size=batch_size, shuffle=True) # Fine-tuning history = model.fit_generator( train_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=validation_generator, nb_val_samples=nb_val_samples) model.save_weights(os.path.join(result_dir, '20180802.h5')) save_history(history, os.path.join(result_dir, '20180802.txt'))
- 使用したCNNモデル：
走行テスト部分
*ラズベリーパイ3
webカメラから動画のストリーミングを行う．
*サーバ
ストリーミングされた動画をキャプチャし，その画像を学習済モデルへ入力し分類を行う．その分類結果をMQTTを用いてモータの制御信号を送信する．
#!/usr/bin/env python#!/usr/bin/env python#coding:utf-8import cv2import matplotlib.pyplot as pltfrom IPython import display import numpy as npfrom io import BytesIOfrom PIL import Imagefrom PIL import ImageOps from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictionsfrom keras.preprocessing import imageimport time import socketimport numpy as npimport cv2#for predictionimport osimport sysfrom keras.applications.vgg16 import VGG16from keras.models import Sequential, Modelfrom keras.layers import Input, Activation, Dropout, Flatten, Densefrom keras.preprocessing import image import paho.mqtt.client as mqttimport threading# Load VGG16#model = VGG16(weights='imagenet')result_dir = 'results'classes = ['foward_1', 'foward_2', 'left_1', 'left_2', 'right_1', 'right_2', 'other'] nb_classes = len(classes) img_height, img_width = 150, 150channels = 3 pre_count = [] # VGG16input_tensor = Input(shape=(img_height, img_width, channels))vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor) # FCfc = Sequential()fc.add(Flatten(input_shape=vgg16.output_shape[1:]))fc.add(Dense(256, activation='relu'))fc.add(Dropout(0.5))fc.add(Dense(nb_classes, activation='softmax')) # VGG16とFCを接続model = Model(input=vgg16.input, output=fc(vgg16.output)) # 学習済みの重みをロードmodel.load_weights(os.path.join(result_dir, '20180803_3.h5')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) URL = "http://192.168.0.4:8080/?action=stream"vc = cv2.VideoCapture(URL)#/////////////////////////////////////////////////////////// def cap (frame, client): frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # makes the blues image look real colored #webcam_preview.set_data(frame) plt.draw() # display.clear_output(wait=True) # display.display(plt.gcf()) plt.pause(0.01) img = Image.fromarray(np.uint8(frame)) img = img.resize((150, 150)) x = image.img_to_array(img) pred_data = np.expand_dims(x, axis=0) #print("show") plt.imshow(img) #plt.show() # print("[------------------------ PREDICT ------------------------]\n") # preds = model.predict(preprocess_input(pred_data)) # #time.sleep(1) # #print(preds) # results = decode_predictions(preds, top=1)[0] # for result in results: # #print(result) # if results[0] == "[n03814639]" or "[n03483316]": # print("s") # print("[{}] {:<30} {}%".format(result[0c], result[1],round(result[2]*100, 2))) pred = model.predict(pred_data)[0] #print(type(int(pred.argsort()[-1:][::-1]))) #from pynput.keyboard import Key, Listener #print("pre_count",len(pre_count)) # host = '192.168.0.18' # port = 1883 # keepalive = 60 # topic = 'topic/moter/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) if int(pred.argsort()[-1:][::-1]) == 0: print('foward_1') client.publish(topic, str(310) + "," + str(300)) #time.sleep(0.2) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 1: print('foward_2') client.publish(topic, str(410) + "," + str(400)) #time.sleep(0.2) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 2: print('left_1') client.publish(topic, str(500) + "," + str(200)) #time.sleep(0.2) # client.publish(topic, str(40) + "," + str(50)) # time.sleep(0.7) # client.publish(topic, str(60) + "," + str(25)) # time.sleep(0.3) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 3: print('left_2') client.publish(topic, str(300) + "," + str(200)) #time.sleep(0.2) # client.publish(topic, str(40) + "," + str(50)) # time.sleep(0.7) # client.publish(topic, str(60) + "," + str(25)) # time.sleep(0.3) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 4: print('right_1') client.publish(topic, str(200) + "," + str(500)) #time.sleep(0.2) # client.publish(topic, str(50) + "," + str(40)) # time.sleep(0.7) # client.publish(topic, str(25) + "," + str(60)) # time.sleep(0.3) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 5: print('right_2') client.publish(topic, str(200) + "," + str(300)) #time.sleep(0.2) # client.publish(topic, str(50) + "," + str(40)) # time.sleep(0.7) # client.publish(topic, str(25) + "," + str(60)) # time.sleep(0.3) #client.publish(topic, str(0) + "," + str(0)) elif int(pred.argsort()[-1:][::-1]) == 6: print('other') client.publish(topic, str(-200) + "," + str(300)) #予測確率が高いトップ5を出力 top = 3 top_indices = pred.argsort()[-top:][::-1] result = [(classes[i], pred[i]) for i in top_indices] print(result) print("") #///////////////////////////////////////////////////////# while True:# ret, img = vc.read()# cv2.imshow("Stream Video",img)# print(img.shape) # Capture webcamera photo# vc = cv2.VideoCapture(0) host = '192.168.0.17'port = 1883keepalive = 60topic = 'topic/motor/dt'client = mqtt.Client()client.connect(host, port, keepalive) if vc.isOpened(): # try to get the first frame is_capturing, frame = vc.read() #cv2.imshow("Stream Video",frame) #frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # makes the blues image look real colored #webcam_preview = plt.imshow(frame) else: is_capturing = False # Push ■ Button!!while is_capturing: try: # Lookout for a keyboardInterrupt to stop the script #print("pre") # time.sleep(3) is_capturing, frame = vc.read() pre_count.append(1) if int(len(pre_count)) % 5 == 0: # thread = threading.Thread(target=cap, args=(frame,)) # thread.start() cap(frame, client) # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # makes the blues image look real colored # #webcam_preview.set_data(frame) # plt.draw() # # display.clear_output(wait=True) # # display.display(plt.gcf()) # #plt.pause(0.01) # img = Image.fromarray(np.uint8(frame)) # img = img.resize((150, 150)) # x = image.img_to_array(img) # pred_data = np.expand_dims(x, axis=0) # print("show") # plt.imshow(img) # # plt.show() # # print("[------------------------ PREDICT ------------------------]\n") # # preds = model.predict(preprocess_input(pred_data)) # # #time.sleep(1) # # #print(preds) # # results = decode_predictions(preds, top=1)[0] # # for result in results: # # #print(result) # # if results[0] == "[n03814639]" or "[n03483316]": # # print("s") # # print("[{}] {:<30} {}%".format(result[0c], result[1],round(result[2]*100, 2))) # pred = model.predict(pred_data)[0] # #print(type(int(pred.argsort()[-1:][::-1]))) # #from pynput.keyboard import Key, Listener # print("pre_count",len(pre_count)) # # host = '192.168.0.18' # # port = 1883 # # keepalive = 60 # # topic = 'topic/moter/dt' # # client = mqtt.Client() # # client.connect(host, port, keepalive) # if int(pred.argsort()[-1:][::-1]) == 0: # print('foward_1') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(200) + "," + str(200)) # #time.sleep(0.2) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 1: # print('foward_2') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(200) + "," + str(200)) # #time.sleep(0.2) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 2: # print('left_1') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(300) + "," + str(100)) # #time.sleep(0.2) # # client.publish(topic, str(40) + "," + str(50)) # # time.sleep(0.7) # # client.publish(topic, str(60) + "," + str(25)) # # time.sleep(0.3) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 3: # print('left_2') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(200) + "," + str(50)) # #time.sleep(0.2) # # client.publish(topic, str(40) + "," + str(50)) # # time.sleep(0.7) # # client.publish(topic, str(60) + "," + str(25)) # # time.sleep(0.3) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 4: # print('right_1') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(100) + "," + str(300)) # #time.sleep(0.2) # # client.publish(topic, str(50) + "," + str(40)) # # time.sleep(0.7) # # client.publish(topic, str(25) + "," + str(60)) # # time.sleep(0.3) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 5: # print('right_2') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(50) + "," + str(200)) # #time.sleep(0.2) # # client.publish(topic, str(50) + "," + str(40)) # # time.sleep(0.7) # # client.publish(topic, str(25) + "," + str(60)) # # time.sleep(0.3) # #client.publish(topic, str(0) + "," + str(0)) # elif int(pred.argsort()[-1:][::-1]) == 6: # print('other') # host = '192.168.0.17' # port = 1883 # keepalive = 60 # topic = 'topic/motor/dt' # client = mqtt.Client() # client.connect(host, port, keepalive) # client.publish(topic, str(200) + "," + str(50)) #import paho.mqtt.client as mqtt #from pynput.keyboard import Key, Listener # host = '192.168.0.7' # port = 1883 # keepalive = 60 # topic = 'mqtt/test' # client = mqtt.Client() # client.connect(host, port, keepalive) # if classes[i] == 'foward': # print('foward') # #client.publish(topic, str(0.5) + "," + str(0) + "," + str(0) + "," + str(0)) # elif classes[i] == 'right': # print('right') # #client.publish(topic, str(0.5) + "," + str(0) + "," + str(0) + "," + str(0)) # elif classes[i] == 'left': # print('left') #client.publish(topic, str(0.5) + "," + str(0) + "," + str(0) + "," + str(0)) #client.publish(topic, str(0.5) + "," + str(0) + "," + str(0) + "," + str(0)) # the pause time is = 1 / frameratef except KeyboardInterrupt: vc.release() is_capturing = False
*EV3
受信した制御信号をモータへ反映し，モータを駆動させ制御を行う．
# !/usr/bin/env python3 import paho.mqtt.client as mqtt from ev3dev.auto import * count = [] ma = Motor('outA') md = Motor('outD') def on_connect(client, userdata, flags, rc): print("Connected with result code " + str(rc)) client.subscribe("topic/motor/dt") def on_message(client, userdata, msg): msg_str = msg.payload.decode("utf-8") msg_array = msg_str.split(",") ma.speed_sp = msg_array[0] md.speed_sp = msg_array[1] count.append(1) print(len(count)) print(msg_array) #ma.duty_cycle_sp = msg_array[0] #time.sleep(3) #ma.stop() #md.duty_cycle_sp = msg_array[1] # time.sleep(3) #ma.stop() ma.run_timed(time_sp=250,speed_sp=ma.speed_sp,stop_action='brake') md.run_timed(time_sp=250,speed_sp=md.speed_sp,stop_action='brake') #time.sleep(1) client = mqtt.Client() client.connect("192.168.0.17" ,1883 ,60) client.on_connect = on_connect client.on_message = on_message ma.run_direct() md.run_direct() ma.duty_cycle_sp = 0 md.duty_cycle_sp = 0 client.loop_forever()
