I build a image classification model in R by keras for R.

Got about 98% accuracy, while got terrible accuracy in python.

Keras version for R is 2.1.3, and 2.1.5 in python

following is the R model code:

model=keras_model_sequential()
model=model %>% layer_conv_2d(filters = 32,kernel_size = c(3,3),padding = 'same',input_shape = c(187,256,3),activation = 'elu')%>%layer_max_pooling_2d(pool_size = c(2,2)) %>%layer_dropout(.25) %>% layer_batch_normalization() %>%layer_conv_2d(filters = 64,kernel_size = c(3,3),padding = 'same',activation = 'relu') %>%layer_max_pooling_2d(pool_size = c(2,2)) %>%layer_dropout(.25) %>% layer_batch_normalization() %>% layer_flatten() %>%layer_dense(128,activation = 'relu') %>%layer_dropout(.25)%>%layer_batch_normalization() %>%layer_dense(6,activation = 'softmax')model %>%compile(loss='categorical_crossentropy',optimizer='adam',metrics='accuracy'
)

I try to rebuild a same model in python, with same input data.

While, got totally different performance. The accuracy even less than 30%

Because R keras is calling python for run keras. With same model architecture, they should get similar performance.

I wonder if this issue caused by preprocess, but still show my python code:

model=Sequential()
model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(187,256,3),padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(Dense(len(label[1]), activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

This is a simple classification. I just do as same as most instruction.

Can not find others faced same problem. So want to ask how it happen and how to solve. Thx

That is a dramatic difference so perhaps there's a bug in the code or something unexpected in the data but reproducing Keras results from R in Python is more difficult than it may seem since setting the seed on the R side is insufficient. Instead of set.seed you should use use_session_with_seed, which comes with the R libraries for tensorflow and keras. Note that for full reproducibility you need to use_session_with_seed(..., disable_gpu=TRUE, disable_parallel_cpu=TRUE). See also stack and tf docs. Also, here is an example using the github version of kerasformula and a public dataset. Also, watch out for functions like layer_dropout that accept seed as a parameter.