官术网_书友最值得收藏!

Getting ready

To understand the impact of varying the optimizer on network accuracy, let's contrast the scenario laid out in previous sections (which was the Adam optimizer) with using a stochastic gradient descent optimizer in this section, while reusing the same MNIST training and test datasets that were scaled (the same data-preprocessing steps as those of step 1 and step 2 in the Scaling the dataset recipe):

model = Sequential()
model.add(Dense(1000, input_dim=784, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=32, verbose=1)

Note that when we used the stochastic gradient descent optimizer in the preceding code, the final accuracy after 100 epochs is ~98% (the code to generate the plots in the following diagram remains the same as the code we used in step 8 of the Training a vanilla neural network recipe):

However, we should also note that the model achieved the high accuracy levels much more slowly when compared to the model that used Adam optimization.

主站蜘蛛池模板: 泸溪县| 离岛区| 安多县| 裕民县| 孟州市| 福海县| 武川县| 蓝田县| 伊吾县| 新乡市| 德江县| 佛学| 齐齐哈尔市| 霞浦县| 边坝县| 通江县| 环江| 秦安县| 涿州市| 凤凰县| 嘉义县| 慈溪市| 广河县| 滦平县| 翁牛特旗| 延庆县| 福泉市| 临湘市| 常宁市| 寿光市| 富源县| 隆尧县| 同仁县| 汉中市| 宜兰市| 澄江县| 淅川县| 江永县| 确山县| 泊头市| 涟源市|