Python convert_to_one_hotの例

コード例 #1

ファイル: main.py プロジェクト: Cracroule/deeplearning_work

def main():
    if DO_TEST: test()
    model = ResNet50(input_shape=(64, 64, 3), classes=6)
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    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.

    # Convert training and test labels to one hot matrices
    Y_train = convert_to_one_hot(Y_train_orig, 6).T
    Y_test = convert_to_one_hot(Y_test_orig, 6).T

    print("number of training examples = " + str(X_train.shape[0]))
    print("number of test examples = " + str(X_test.shape[0]))
    print("X_train shape: " + str(X_train.shape))
    print("Y_train shape: " + str(Y_train.shape))
    print("X_test shape: " + str(X_test.shape))
    print("Y_test shape: " + str(Y_test.shape))

    model.fit(X_train, Y_train, epochs=7, batch_size=64)
    my_preds = model.evaluate(X_test, Y_test)
    print("My Loss = " + str(my_preds[0]))
    print("My Test Accuracy = " + str(my_preds[1]))

    model = load_model('../trained_model/ResNet50.h5')
    preds = model.evaluate(X_test, Y_test)
    print("Their Loss = " + str(preds[0]))
    print("Their Test Accuracy = " + str(preds[1]))

コード例 #2

ファイル: part3.py プロジェクト: Frostjon/RestNet50-homework-

def Load_Model(themodel):
    model = load_model(themodel)

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

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

    # Convert training and test labels to one hot matrices
    Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
    Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T

    # 评估模型
    preds = model.evaluate(X_test, Y_test)

    print("误差值 = " + str(preds[0]))
    print("准确率 = " + str(preds[1]))

    return model

コード例 #3

ファイル: part3.py プロジェクト: Frostjon/RestNet50-homework-

def Train_Model():
    # print(datetime.datetime.now())
    # 实体化并编译
    model = ResNet50(input_shape=(64, 64, 3), classes=6)
    model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

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

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

    # Convert training and test labels to one hot matrices
    Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
    Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T

    print("number of training examples = " + str(X_train.shape[0]))
    print("number of test examples = " + str(X_test.shape[0]))
    print("X_train shape: " + str(X_train.shape))
    print("Y_train shape: " + str(Y_train.shape))
    print("X_test shape: " + str(X_test.shape))
    print("Y_test shape: " + str(Y_test.shape))

    """
    number of training examples = 1080
    number of test examples = 120
    X_train shape: (1080, 64, 64, 3)
    Y_train shape: (1080, 6)
    X_test shape: (120, 64, 64, 3)
    Y_test shape: (120, 6)
    """

    # 训练
    model.fit(X_train, Y_train, epochs=40, batch_size=32)

    # 评估模型
    preds = model.evaluate(X_test, Y_test)

    print("误差值 = " + str(preds[0]))
    print("准确率 = " + str(preds[1]))

コード例 #4

ファイル: Keras_ResNet.py プロジェクト: Geophysics-Liu/Deep_Learning

    model = Model(inputs=input, outputs=output)

    return model


if __name__ == '__main__':
    X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = resnets_utils.load_dataset(
    )

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

    # Convert training and test labels to one hot matrices
    Y_train = resnets_utils.convert_to_one_hot(Y_train_orig, 6).T
    Y_test = resnets_utils.convert_to_one_hot(Y_test_orig, 6).T

    print("number of training examples = " + str(X_train.shape[0]))
    print("number of test examples = " + str(X_test.shape[0]))
    print("X_train shape: " + str(X_train.shape))
    print("Y_train shape: " + str(Y_train.shape))
    print("X_test shape: " + str(X_test.shape))
    print("Y_test shape: " + str(Y_test.shape))

    model = ResNet50(input_shape=(64, 64, 3), classes=6)

    model.summary()

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

コード例 #5

ファイル: ResidualNetworks.py プロジェクト: xiaoxiaopingzi/DeepLearning

    return model


model = ResNet50(input_shape=(64, 64, 3), classes=6)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

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.

# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T

print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))

start_time = time.time()
model.fit(X_train, Y_train, epochs=10, batch_size=32)
end_time = time.time()
print("训练模型所花的时间为：%d秒" % (end_time - start_time))

preds = model.evaluate(X_test, Y_test)