0

我知道这个错误是反复出现的，我知道是什么原因造成的。例如，用 163 张 150x150 的图像运行这个模型会给我错误（但是我不清楚为什么设置 batch_size Keras 似乎仍然试图在 GPU 中一次分配所有图像）：

model = Sequential()
    model.add(Conv2D(64, kernel_size=(6, 6), activation='relu', input_shape=input_shape, padding='same', name='b1_conv'))
    model.add(MaxPooling2D(pool_size=(2, 2), name='b1_poll'))
    model.add(Conv2D(128, kernel_size=(6, 6), activation='relu', padding='same', name='b2_conv'))
    model.add(MaxPooling2D(pool_size=(2, 2), name='b2_pool'))
    model.add(Conv2D(256, kernel_size=(6, 6), activation='relu', padding='same', name='b3_conv'))
    model.add(MaxPooling2D(pool_size=(2, 2), name='b3_pool'))
    model.add(Flatten())
    model.add(Dense(500, activation='relu', name='fc1'))
    model.add(Dropout(0.5))
    model.add(Dense(500, activation='relu', name='fc2'))
    model.add(Dropout(0.5))
    model.add(Dense(n_targets, activation='softmax', name='prediction'))
    model.compile(optimizer=optim, loss='categorical_crossentropy',  metrics=['accuracy'])

鉴于此，我将图像尺寸减小到 30x30（这导致精度下降，正如预期的那样）。但是，在此模型中运行网格搜索资源耗尽。

model = KerasClassifier(build_fn=create_model, verbose=0)

# grid initial weight, batch size and optimizer
sgd  = optimizers.SGD(lr=0.0005)
rms  = optimizers.RMSprop(lr=0.0005)
adag = optimizers.Adagrad(lr=0.0005)
adad = optimizers.Adadelta(lr=0.0005)
adam = optimizers.Adam(lr=0.0005)
adamm = optimizers.Adamax(lr=0.0005)
nadam = optimizers.Nadam(lr=0.0005)

optimizers = [sgd, rms, adag, adad, adam, adamm, nadam]
init = ['glorot_uniform', 'normal', 'uniform', 'he_normal']
batches = [32, 64, 128]
param_grid = dict(optim=optimizers, batch_size=batches, init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

我想知道是否有可能在网格搜索使用的每个组合之前“清理”一些东西（不确定我是否说清楚了，这对我来说是全新的）。

编辑

使用fit_generator也给了我同样的错误：

def generator(features, labels, batch_size):
    # Create empty arrays to contain batch of features and labels#
    batch_features = np.zeros((batch_size, size, size, 1))
    batch_labels = np.zeros((batch_size, n_targets))
    while True:
        for i in range(batch_size):
            # choose random index in features
            index = np.random.choice(len(features),1)
            batch_features[i] = features[index]
            batch_labels[i] = labels[index]
        yield batch_features, batch_labels

sgd  = optimizers.SGD(lr=0.0005)
rms  = optimizers.RMSprop(lr=0.0005)
adag = optimizers.Adagrad(lr=0.0005)
adad = optimizers.Adadelta(lr=0.0005)
adam = optimizers.Adam(lr=0.0005)
adamm = optimizers.Adamax(lr=0.0005)
nadam = optimizers.Nadam(lr=0.0005)

optim = [rms, adag, adad, adam, adamm, nadam]
init = ['normal', 'uniform', 'he_normal']

combinations = [(a, b) for a in optim for b in init]
for combination in combinations:
    init  = combination[1]
    optim = combination[0]
    model = create_model(init=init, optim=optim)
    model.fit_generator(generator(X_train, y_train, batch_size=32),
                        steps_per_epoch=X_train.shape[0] // 32,
                        epochs=100, verbose=0, validation_data=(X_test, y_test))
    scores = model.model.evaluate(X_test, y_test, verbose=0)
    print("%s: %.2f%% Model %s %s" % (model.model.metrics_names[1], scores[1]*100, optim, init))
4

2 回答 2

2

您应该使用生成器 + yield，它们会从内存中丢弃它们已经使用的数据。查看我对类似问题的回答。

于 2017-06-29T13:43:14.773 回答
1

每次训练/评估运行后，您必须清除 tensorflow 会话

K.clear_session()

作为tensorflowK后端。

于 2017-07-03T17:43:01.943 回答