I was developing a CNN with tensorflow in Kaggle with a P100 GPU, it obtained a val accuracy of .87 and a loss of .56. When I downloaded it and tested it on my PC (which does not have a GPU that tensorflow can use) I noticed that Its performance declines, an image that predicted correctly in Kaggle has many errors when predicting on my PC. Why would it be? I think that due to the fact of not predicting images using a GPU, but I would like to know the opinion of someone more experienced
To be more sure what it was, I made a prediction with an image that I took from the training dataset. At the time of training, it obtained an accuracy of .92. The bad thing is that it did not predict it well.
This is my model:
model = Sequential()
model.add(Conv2D(32, (2,2), 1, activation='relu', input_shape=(256, 256, 3), kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Conv2D(64, (2,2), 2, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.2))
model.add(Conv2D(128, (2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.2))
model.add(Conv2D(128,(2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256,(2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.2))
model.add(Conv2D(256, (2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, (2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.2))
model.add(Conv2D(64, (2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, (2,2), 1, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dropout(0.2))
# model.add(Dense(128, activation='relu'))
# model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
# model.add(Dropout(0.2))
model.add(Dense(11, activation='softmax'))
model.summary()
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