i am working on a regressor problem with 50,000 rows and 20 coloumns ,i want to implement CNN and find MSE on different architecture, model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='tanh')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='mean_squared_error', optimizer=sgd) model.fit(Bx_train, Fx_train, batch_size=32, epochs=10) score = model.evaluate(Bx_test, Fx_test, batch_size=32), what dimension sholud i used as input

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i am working on a regressor problem with 50,000 rows and 20 coloumns ,i want to implement CNN and find MSE on different architecture, model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='tanh')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='mean_squared_error', optimizer=sgd) model.fit(Bx_train, Fx_train, batch_size=32, epochs=10) score = model.evaluate(Bx_test, Fx_test, batch_size=32), what dimension sholud i used as input #10518

Closed prateekbhadauria closed 6 years ago

prateekbhadauria commented 6 years ago

Please make sure that the boxes below are checked before you submit your issue. If your issue is an implementation question, please ask your question on StackOverflow or join the Keras Slack channel and ask there instead of filing a GitHub issue.

Thank you!

[ ] Check that you are up-to-date with the master branch of Keras. You can update with: pip install git+git://github.com/keras-team/keras.git --upgrade --no-deps
[ ] If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found here.
[ ] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with: pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
[ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

chrishna1 commented 6 years ago

According to your architecture input shape should be (100,100,3). Also please read this : Please make sure that the boxes below are checked before you submit your issue. If your issue is an implementation question, please ask your question on StackOverflow or join the Keras Slack channel and ask there instead of filing a GitHub issue.

Dref360 commented 6 years ago

Hi! This issue isn't related to a bug/enhancement/feature request or other accepted types of issue.

To ask questions, please see the following resources :

Thanks!

If you think I made a mistake, please re-open this issue.