回答編集履歴

コード追記

2020/02/16 02:03

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8524ba23

スコア38341

test CHANGED Viewed

@@ -29,3 +29,435 @@
 参考まで。
+予測データ作成
+```Python
+'''
+#Train a simple deep CNN on the CIFAR10 small images dataset.
+It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
+(it's still underfitting at that point, though).
+'''
+import keras
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+import os
+batch_size = 32
+num_classes = 10
+epochs = 100
+data_augmentation = True
+num_predictions = 20
+#save_dir = os.path.join(os.getcwd(), 'saved_models')
+save_dir = "."
+model_name = 'cnn_model.h5'
+w_name = 'cnn_weights.h5'
+# The data, split between train and test sets:
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+# Convert class vectors to binary class matrices.
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+model = Sequential()
+model.add(Conv2D(32, (3, 3), padding='same',
+                 input_shape=x_train.shape[1:]))
+model.add(Activation('relu'))
+model.add(Conv2D(32, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Conv2D(64, (3, 3), padding='same'))
+model.add(Activation('relu'))
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes))
+model.add(Activation('softmax'))
+# initiate RMSprop optimizer
+opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
+# Let's train the model using RMSprop
+model.compile(loss='categorical_crossentropy',
+              optimizer=opt,
+              metrics=['accuracy'])
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+if not data_augmentation:
+    print('Not using data augmentation.')
+    model.fit(x_train, y_train,
+              batch_size=batch_size,
+              epochs=epochs,
+              validation_data=(x_test, y_test),
+              shuffle=True)
+else:
+    print('Using real-time data augmentation.')
+    # This will do preprocessing and realtime data augmentation:
+    datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        zca_epsilon=1e-06,  # epsilon for ZCA whitening
+        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+        # randomly shift images horizontally (fraction of total width)
+        width_shift_range=0.1,
+        # randomly shift images vertically (fraction of total height)
+        height_shift_range=0.1,
+        shear_range=0.,  # set range for random shear
+        zoom_range=0.,  # set range for random zoom
+        channel_shift_range=0.,  # set range for random channel shifts
+        # set mode for filling points outside the input boundaries
+        fill_mode='nearest',
+        cval=0.,  # value used for fill_mode = "constant"
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=False,  # randomly flip images
+        # set rescaling factor (applied before any other transformation)
+        rescale=None,
+        # set function that will be applied on each input
+        preprocessing_function=None,
+        # image data format, either "channels_first" or "channels_last"
+        data_format=None,
+        # fraction of images reserved for validation (strictly between 0 and 1)
+        validation_split=0.0)
+    # Compute quantities required for feature-wise normalization
+    # (std, mean, and principal components if ZCA whitening is applied).
+    datagen.fit(x_train)
+    # Fit the model on the batches generated by datagen.flow().
+    model.fit_generator(datagen.flow(x_train, y_train,
+                                     batch_size=batch_size),
+                        epochs=epochs,
+                        validation_data=(x_test, y_test),
+                        workers=4)
+# Save model and weights
+if not os.path.isdir(save_dir):
+    os.makedirs(save_dir)
+model_path = os.path.join(save_dir, model_name)
+model.save(model_path)
+print('Saved trained model at %s ' % model_path)
+w_path = os.path.join(save_dir, w_name)
+model.save_weights(w_path)
+# Score trained model.
+scores = model.evaluate(x_test, y_test, verbose=1)
+print('Test loss:', scores[0])
+print('Test accuracy:', scores[1])
+```
+環境構築とテスト実行
+```
+>conda create -n cifar10 python=3.6.9 tensorflow=1.15.0 keras=2.2.5 matplotlib flask
+>conda activate cifar10
+(VS2015ビルド環境がないと怒られるが無視)
+>conda install -c conda-forge opencv
+>python app.py
+(いろいろ警告が出るが無視)
+```
+`app.py`
+```Python
+import pickle
+import numpy as np
+from flask import Flask, render_template, request, redirect, url_for
+from keras.models import load_model
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import io
+import tensorflow as tf
+import sys
+app = Flask(__name__)
+# 機械学習モデルの読込
+model = load_model("./templates/cnn_model.h5")
+model.load_weights("./templates/cnn_weights.h5")
+graph = tf.get_default_graph()
+@app.route("/")
+def index():
+    return render_template("index.html")
+@app.route("/result", methods=['POST'])
+def result():
+    if request.files["image"]:
+        img_file = request.files["image"]
+        f = img_file.stream.read()
+        bin_data = io.BytesIO(f)
+        file_bytes = np.asarray(bytearray(bin_data.read()), dtype=np.uint8)
+        img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        img = cv2.resize(img, (32, 32))
+        in_rows = 32
+        in_cols = 32
+        in_colors = 3
+        labels = [
+            'airplane',
+            'automobile',
+            'bird',
+            'cat',
+            'deer',
+            'dog',
+            'frog',
+            'horse',
+            'ship',
+            'truck'
+        ]
+        img = img.reshape(-1, in_rows, in_cols,
+                          in_colors).astype("float32") / 255
+        with graph.as_default():
+            r = model.predict(img, batch_size=32, verbose=1)
+            res = r[0]
+            print(res, file=sys.stdout)
+        return render_template("result.html", res=res, labels=labels)
+    else:
+        return redirect(url_for('index'))
+if __name__ == '__main__':
+    app.run(debug=True)
+```