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トップに関する質問opencv python 画像の判別について

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事前学習のコードを追加

2019/04/14 05:23

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+ 事前にmlpとcnnで学習済みです
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  ```python
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+ from keras.datasets import cifar10
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+ (x_train, y_train), (x_test, y_test) = cifar10.load_data()
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+ import matplotlib.pyplot as plt
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+ from PIL import Image
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+ plt.figure(figsize=(10, 10))
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+ labels = ["airplene", "automobile", "bord", "cat", "deer", "dog", "frog", "hprse", "ship", "truck"]
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+ for i in range(0, 40):
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+ im = Image.fromarray(x_train[i])
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+ plt.subplot(5, 8, i + 1)
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+ plt.title(labels[y_train[i][0]])
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+ plt.tick_params(labelbottom="off",bottom="off")
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+ plt.tick_params(labelleft="off",left="off")
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+ plt.imshow(im)
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+ plt.show()
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+ import matplotlib.pyplot as plt
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+ import keras
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+ from keras.datasets import cifar10
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+ from keras.models import Sequential
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+ from keras.layers import Dense, Dropout
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+ num_classes = 10
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+ im_rows = 32
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+ im_cols = 32
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+ im_size = im_rows * im_cols * 3
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+ # データを読み込む --- (*1)
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+ (X_train, y_train), (X_test, y_test) = cifar10.load_data()
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+ # データを一次元配列に変換 --- (*2)
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+ X_train = X_train.reshape(-1, im_size).astype('float32') / 255
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+ X_test = X_test.reshape(-1, im_size).astype('float32') / 255
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+ # ラベルデータをOne-Hot形式に変換
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+ y_train = keras.utils.to_categorical(y_train, num_classes)
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+ y_test = keras.utils.to_categorical(y_test, num_classes)
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+ # モデルを定義 --- (*3)
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+ model = Sequential()
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+ model.add(Dense(512, activation='relu', input_shape=(im_size,)))
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+ model.add(Dense(num_classes, activation='softmax'))
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+ # モデルをコンパイル --- (*4)
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+ model.compile(
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+ loss='categorical_crossentropy',
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+ optimizer='adam',
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+ metrics=['accuracy'])
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+ # 学習を実行 --- (*5)
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+ hist = model.fit(X_train, y_train,
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+ batch_size=32, epochs=50,
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+ verbose=1,
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+ validation_data=(X_test, y_test))
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+ # モデルを評価 --- (*6)
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+ score = model.evaluate(X_test, y_test, verbose=1)
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+ print('正解率=', score[1], 'loss=', score[0])
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+ # 学習の様子をグラフへ描画 --- (*7)
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+ plt.plot(hist.history['acc'])
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+ plt.plot(hist.history['val_acc'])
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+ plt.title('Accuracy')
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+ plt.legend(['train', 'test'], loc='upper left')
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+ plt.show()
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+ plt.plot(hist.history['loss'])
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+ plt.plot(hist.history['val_loss'])
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+ plt.title('Loss')
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+ plt.legend(['train', 'test'], loc='upper left')
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+ plt.show()
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+ import matplotlib.pyplot as plt
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+ import keras
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+ from keras.datasets import cifar10
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+ from keras.models import Sequential
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+ from keras.layers import Dense, Dropout, Activation, Flatten
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+ from keras.layers import Conv2D, MaxPooling2D
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+ num_classes = 10
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+ im_rows = 32
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+ im_cols = 32
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+ in_shape = (im_rows, im_cols, 3)
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+ # データを読み込む --- (*1)
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+ (X_train, y_train), (X_test, y_test) = cifar10.load_data()
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+ # データを正規化 --- (*2)
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+ X_train = X_train.astype('float32') / 255
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+ X_test = X_test.astype('float32') / 255
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+ # ラベルデータをOne-Hot形式に変換
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+ y_train = keras.utils.to_categorical(y_train, num_classes)
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+ y_test = keras.utils.to_categorical(y_test, num_classes)
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+ # モデルを定義 --- (*3)
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+ model = Sequential()
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+ model.add(Conv2D(32, (3, 3), padding='same',
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+ input_shape=in_shape))
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+ model.add(Activation('relu'))
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+ model.add(Conv2D(32, (3, 3)))
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+ model.add(Activation('relu'))
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+ model.add(MaxPooling2D(pool_size=(2, 2)))
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+ model.add(Dropout(0.25))
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+ model.add(Conv2D(64, (3, 3), padding='same'))
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+ model.add(Activation('relu'))
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+ model.add(Conv2D(64, (3, 3)))
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+ model.add(Activation('relu'))
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+ model.add(MaxPooling2D(pool_size=(2, 2)))
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+ model.add(Dropout(0.25))
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+ model.add(Flatten())
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+ model.add(Dense(512))
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+ model.add(Activation('relu'))
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+ model.add(Dropout(0.5))
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+ model.add(Dense(num_classes))
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+ model.add(Activation('softmax'))
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+ # モデルをコンパイル --- (*4)
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+ model.compile(
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+ loss='categorical_crossentropy',
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+ optimizer='adam',
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+ metrics=['accuracy'])
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+ # 学習を実行 --- (*5)
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+ hist = model.fit(X_train, y_train,
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+ batch_size=32, epochs=50,
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+ verbose=1,
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+ validation_data=(X_test, y_test))
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+ # モデルを評価 --- (*6)
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+ score = model.evaluate(X_test, y_test, verbose=1)
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+ print('正解率=', score[1], 'loss=', score[0])
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+ # 学習の様子をグラフへ描画 --- (*7)
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+ plt.plot(hist.history['acc'])
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+ plt.plot(hist.history['val_acc'])
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+ plt.title('Accuracy')
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+ plt.legend(['train', 'test'], loc='upper left')
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+ plt.show()
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+ plt.plot(hist.history['loss'])
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+ plt.plot(hist.history['val_loss'])
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+ plt.title('Loss')
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+ plt.legend(['train', 'test'], loc='upper left')
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+ plt.show()
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+ model.save_weights("cifar10-weight.h5")
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+ model.load_weights("cifar10-weight.h5")
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+ model.save_weights("cifar10-mlp-weight.h5")
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  import cv2
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  import numpy as np