keras実現happyhouseプログラム

ng第4課2週目の宿題は、kerosでhappyhouseを実現する、すなわちkerasフレームワークを用いて笑顔と非笑顔を識別するコードは以下の通りである.このコードはトレーニングセットとテストセットの精度が高いが、自分で入力した画像の識別効果は確かによくない.ngは宿題で原因を示している.
The training/test sets were quite similar; for example, all the pictures were taken against the same background (since a front door camera is always mounted in the same position). This makes the problem easier, but a model trained on this data may or may not work on your own data. But feel free to give it a try!
より良いモデルを訓練するには、自分でフレームワークの方法をデバッグしたり、パラメータを調整したり、トレーニングセットを再選択したりする必要があります.

import keras.backend as K
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from keras.utils import plot_model
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot

K.set_image_data_format('channels_last')


def mean_pred(y_true,y_pred):
    return K.mean(y_pred)


def load_dataset():
    train_dataset = h5py.File('datasets/train_happy.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:])  # train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:])  # train set labels

    test_dataset = h5py.File('datasets/test_happy.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:])  # test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:])  # test set labels

    classes = np.array(test_dataset["list_classes"][:])  # the list of classes

    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))

    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes


X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.

# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.T


#  graded function : happymodel
def HappyModel(input_shape):
    # input_shape -- shape of the images of the dataset
    # return model -- a Model() instance in keras

    X_input = Input(input_shape)
    X = ZeroPadding2D((3, 3))(X_input)
    X = BatchNormalization(axis=3, name='bn0')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((2, 2), name='max_pool')(X)
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)
    model = Model(inputs=X_input, outputs=X, name='HappyModel')

    return model


# train and test model above
# 1 Create the model by calling the function above
happyModel = HappyModel(X_train.shape[1:])

# 2 Compile the model by calling model.compile(optimizer = "...", loss = "...", metrics = ["accuracy"])
happyModel.compile(optimizer="adam", loss='binary_crossentropy', metrics=['accuracy'])

# 3 Train the model on train data by calling model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)
happyModel.fit(x=X_train, y=Y_train, epochs=40, batch_size=16)

# 4 Test the model on test data by calling model.evaluate(x = ..., y = ...)
preds = happyModel.evaluate(x=X_test, y=Y_test)

print()
print("Loss = " + str(preds[0]))
print("Test Accuracy = " + str(preds[1]))


# test
img_path = 'images/cry.jpg'
img = image.load_img(img_path, target_size=(64, 64))
imshow(img)
plt.show()

x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

print(happyModel.predict(x))

happyModel.summary()

plot_model(happyModel, to_file='HappyModel.png')
SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))