AvantiShri
commented
5 years ago How big is the difference you are observing?
On Tue, Jun 4, 2019 at 5:30 AM Muskaan Jain notifications@github.com wrote:
Hi,
When applying deeplift over the dataset of "cats and dogs" with the same architecture as provided in Mnist examples. The difference in predictions is coming to be a non-zero value.
Model
model = Sequential() model.add(Conv2D(filters=32, kernel_size=(4,4), strides=(2,2), input_shape=(1, 150, 150))) model.add(Activation("relu")) model.add(Conv2D(filters=64, kernel_size=(4,4), strides=(2,2))) model.add(Activation("relu")) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(units=128)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(units=2)) model.add(Activation("softmax")) from keras.optimizers import Adam model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
keras version : 2.2.0 tensorflow version : 1.10.1
I started with some another architecture but it was showing the difference so for a trial, I changed the architecture same as that used in Mnist example but then I am getting the difference again.
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MuskaanJain
Hi,
When applying deeplift over the dataset of "cats and dogs" with the same architecture as provided in Mnist examples. The difference in predictions is coming to be a non-zero value.
Model
model = Sequential() model.add(Conv2D(filters=32, kernel_size=(4,4), strides=(2,2), input_shape=(1, 150, 150))) model.add(Activation("relu")) model.add(Conv2D(filters=64, kernel_size=(4,4), strides=(2,2))) model.add(Activation("relu")) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(units=128)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(units=2)) model.add(Activation("softmax")) from keras.optimizers import Adam model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
keras version : 2.2.0 tensorflow version : 1.10.1
I started with some another architecture but it was showing the difference so for a trial, I changed the architecture same as that used in Mnist example but then I am getting the difference again.