strange loss curves when BatchNormalization used in Keras












0















Part of code:



mobilenetv2 = MobileNetV2(input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet',

                          input_tensor=None,

                          pooling=None,

                          classes=12)



for layer in mobilenetv2.layers:

    layer.trainable = False



last = mobilenetv2.layers[-1].output

x = Flatten()(last)



x = Dense(120, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



x = Dense(84, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



preds = Dense(12, activation='softmax')(x)





model = Model(inputs=mobilenetv2.input, outputs=preds)


but the loss curve:



enter image description here



enter image description here



Are the above curves normal? I did not use dropout layers, because I am asked to compare dropout layers with BatchNormalization. but the curves look strange. Are my codes right? Or anything missing?
Thanks






keras keras-layer batch-normalization







share|improve this question























  • Maybe this answer is relevant.

    – today
    Nov 17 '18 at 15:06
















0















Part of code:



mobilenetv2 = MobileNetV2(input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet',

                          input_tensor=None,

                          pooling=None,

                          classes=12)



for layer in mobilenetv2.layers:

    layer.trainable = False



last = mobilenetv2.layers[-1].output

x = Flatten()(last)



x = Dense(120, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



x = Dense(84, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



preds = Dense(12, activation='softmax')(x)





model = Model(inputs=mobilenetv2.input, outputs=preds)


but the loss curve:



enter image description here



enter image description here



Are the above curves normal? I did not use dropout layers, because I am asked to compare dropout layers with BatchNormalization. but the curves look strange. Are my codes right? Or anything missing?
Thanks






keras keras-layer batch-normalization







share|improve this question























  • Maybe this answer is relevant.

    – today
    Nov 17 '18 at 15:06














0












0








0








Part of code:



mobilenetv2 = MobileNetV2(input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet',

                          input_tensor=None,

                          pooling=None,

                          classes=12)



for layer in mobilenetv2.layers:

    layer.trainable = False



last = mobilenetv2.layers[-1].output

x = Flatten()(last)



x = Dense(120, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



x = Dense(84, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



preds = Dense(12, activation='softmax')(x)





model = Model(inputs=mobilenetv2.input, outputs=preds)


but the loss curve:



enter image description here



enter image description here



Are the above curves normal? I did not use dropout layers, because I am asked to compare dropout layers with BatchNormalization. but the curves look strange. Are my codes right? Or anything missing?
Thanks






keras keras-layer batch-normalization







share|improve this question














Part of code:



mobilenetv2 = MobileNetV2(input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet',

                          input_tensor=None,

                          pooling=None,

                          classes=12)



for layer in mobilenetv2.layers:

    layer.trainable = False



last = mobilenetv2.layers[-1].output

x = Flatten()(last)



x = Dense(120, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



x = Dense(84, use_bias=False)(x)

x = BatchNormalization()(x)

x = Activation('relu')(x)



preds = Dense(12, activation='softmax')(x)





model = Model(inputs=mobilenetv2.input, outputs=preds)


but the loss curve:



enter image description here



enter image description here



Are the above curves normal? I did not use dropout layers, because I am asked to compare dropout layers with BatchNormalization. but the curves look strange. Are my codes right? Or anything missing?
Thanks






keras keras-layer batch-normalization




keras keras-layer batch-normalization






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 17 '18 at 14:35









BAEBAE

2,71562969




2,71562969













  • Maybe this answer is relevant.

    – today
    Nov 17 '18 at 15:06



















  • Maybe this answer is relevant.

    – today
    Nov 17 '18 at 15:06

















Maybe this answer is relevant.

– today
Nov 17 '18 at 15:06





Maybe this answer is relevant.

– today
Nov 17 '18 at 15:06












0






active

oldest

votes











Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53352213%2fstrange-loss-curves-when-batchnormalization-used-in-keras%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























0






active

oldest

votes








0






active

oldest

votes









active

oldest

votes






active

oldest

votes
















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid …



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53352213%2fstrange-loss-curves-when-batchnormalization-used-in-keras%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







-

Popular posts from this blog

Guess what letter conforming each word

Image
6 $begingroup$ This is an entry in the Fortnightly Topic Challenge #41: Short and Sweet. 1 letter only could be mean anything . With an additional letter in front , could be used as medicine component and something you definitely learn or at least heard once. Add another letter in front , you would possibly hear that many times . Add an additional letter in front , you would found an independent country . With an additional letter, in the end , you would found something related to apple . Add another letter in the end , you would found something bad . Hint: Each letter is unique . word knowledge letters chemistry share | improve this question ...
Read more

Port of Spain

Image
For ports in the country of Spain, see List of ports in Spain. City in City of Port of Spain, Trinidad and Tobago Port of Spain City City of Port of Spain Top to bottom (left to right): Skyline of Port of Spain from Gulf of Paria; National Academy for the Performing Arts, Independence Square, Eric Williams Plaza; MovieTowne, Port of Port of Spain Motto(s):  "We Gather Strength As We Go Along" Port of Spain Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Country   Trinidad and Tobago Jurisdiction City of Port of Spain Settled 1560 Incorporated (town) 1853 Incorporated (city) 1914 Government  • Mayor Joel Martinez  • Governing body City Corporation Area [1]  • City 12 km 2 (5 sq mi) Elevation [2] 66 m (217 ft) Population (2011) [1]  • City 37,074  • Rank 3rd  • Density 3,090/km 2 (8,000/sq mi)...
Read more

Run scheduled task as local user group (not BUILTIN)

Image
0 I am trying to run a scheduled task with User Group (not a single user). This is working fine when I in the field "when running a task, use the following user account:" insert BUILTIN User Group (for example: Administrators or Users). But when I try it with the created Local User Group (example: TestGroup) it is not working. The Users in the TestGroup are the same as in the Administrators group. What could be the problem? Are there some restrictions to the Created User Group compared to BUILTINUsers? scheduled-tasks taskscheduler windowstaskschedule share | improve this question asked Nov 15 '18 at 15:36 Far...
Read more