Hi,
I’ve a very simple Keras program to recognize fruit (5 categories). Here’s a part of the source code :
model = Sequential()
...
# define the first (and only) CONV => RELU layer
model.add(Conv2D(32, (3, 3), padding="same",input_shape=inputShape))
model.add(Activation("relu"))
# softmax classifier
model.add(Flatten())
model.add(Dense(classes))
model.add(Activation("softmax"))
It perfectly works. Now I want to design the same CNN with DLS.
Here’s the configuration :
Dataset
Run4
Dataset Name: FruitVertical
Type: private
Samples: 282
Mapping image: {port:InputPort0,shape:,type:Image,options:{Width:28,rotation_range:0,horizontal_flip:false,Augmentation:false,pretrained:None,vertical_flip:false,height_shift_range:0,shear_range:0,width_shift_range:0,Scaling:1,Resize:false,Normalization:false,Height:28}}
fruit: {port:OutputPort0,shape:,type:Categorical,options:{}}
Samples validation: 28
split: 4
test: 28
training: 225
Model
Run4
Output_1
Input_1
Convolution2D_1 nb_col: 3
nb_filter: 32
nb_row: 3
activation: relu
Flatten_1
Dense_1 output_dim: 5
activation: softmax
HyperParamerters
Run4
Epoch 100
Batch Size 32
Custom Loss false
Loss Function categorical_crossentropy
Optimizer lr: 0.005
name: SGD
But after training, all the validation results are the same (kiwi) :
There’s not a lot of photos (225) but, with the same dataset, if I use the AutoML to generate a (more complex) DL model, it works perfectly so the problem is not with my data (and with my Keras application, it works too with the same data).
What is the problem with my network?
Best regards,
Philippe