Python keras.metrics.binary_accuracy() Examples

The following are 3 code examples of keras.metrics.binary_accuracy(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.metrics , or try the search function .
Example #1
Source File: modelfunctions.py    From Learning-aftershock-location-patterns with MIT License 6 votes vote down vote up 
def create_model():
    model = Sequential()
    model.add(Dense(50, input_dim=12, kernel_initializer='lecun_uniform', activation = 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(50, kernel_initializer='lecun_uniform', activation= 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(50, kernel_initializer='lecun_uniform', activation= 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(50, kernel_initializer='lecun_uniform', activation= 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(50, kernel_initializer='lecun_uniform', activation= 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(50, kernel_initializer='lecun_uniform', activation= 'tanh'))
    model.add(Dropout(0.50))
    model.add(Dense(1, kernel_initializer='lecun_uniform', activation='sigmoid'))
    model.compile(optimizer='adadelta', loss='binary_crossentropy', metrics=[metrics.binary_accuracy])
    return model
Example #2
Source File: test_tuning.py    From cs-ranking with Apache License 2.0 5 votes vote down vote up 
def optimizer():
    from ..tunable import Tunable

    class RankerStub(Tunable):
        def fit(self, X, Y, **kwargs):
            self.seed = int(np.sum(list(self.__dict__.values())))

        def predict(self, X, **kwargs):
            random_state = np.random.RandomState(self.seed)
            weight = random_state.rand(n_features, 2)
            scores = np.dot(X, weight) / np.dot(X, weight).sum(axis=1)[:, None]
            return scores.argmax(axis=1)

        def set_tunable_parameters(self, **point):
            self.__dict__.update(point)

        def __call__(self, X, *args, **kwargs):
            return self.predict(X, **kwargs)

    ranker = RankerStub()

    rankers = [RankerStub() for _ in range(2)]
    test_params = {
        rankers[0]: dict(a=(1.0, 4.0)),
        ranker: dict(b=(4.0, 7.0), c=(7.0, 10.0)),
        rankers[1]: dict(d=(10.0, 13.0)),
    }

    opt = ParameterOptimizer(
        learner=ranker,
        optimizer_path=OPTIMIZER_PATH,
        tunable_parameter_ranges=test_params,
        ranker_params=dict(),
        validation_loss=binary_accuracy,
    )
    return opt, rankers, test_params
Example #3
Source File: step2_train_nodule_detector.py    From kaggle_ndsb2017 with MIT License 4 votes vote down vote up 
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model:
    inputs = Input(shape=input_shape, name="input_1")
    x = inputs
    x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x)
    x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x)
    x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x)

    # 2nd layer group
    x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x)
    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x)
    if USE_DROPOUT:
        x = Dropout(p=0.3)(x)

    # 3rd layer group
    x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x)
    x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x)
    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x)
    if USE_DROPOUT:
        x = Dropout(p=0.4)(x)

    # 4th layer group
    x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x)
    x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x)
    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x)
    if USE_DROPOUT:
        x = Dropout(p=0.5)(x)

    last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x)
    out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64)
    out_class = Flatten(name="out_class")(out_class)

    out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64)
    out_malignancy = Flatten(name="out_malignancy")(out_malignancy)

    model = Model(input=inputs, output=[out_class, out_malignancy])
    if load_weight_path is not None:
        model.load_weights(load_weight_path, by_name=False)
    model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error})

    if features:
        model = Model(input=inputs, output=[last64])
    model.summary(line_length=140)

    return model