Hi,
Of course, I will do it.
I now have another problem by training the model (on cloud). The process stop with this error:
The training process start some times for a few seconds, then stop with the message. Some other times it stop at the beginning. I think it has with the data (images) to do.
Is it possible that the resize function on cloud do not run like expected?
My model:
import keras
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.core import Dropout
from keras.layers.core import Dense
from keras.layers.core import Flatten
from keras.layers import Input
from keras.models import Model
from keras.regularizers import *
def get_model():
aliases = {}
Input_9 = Input(shape=(3, 28, 28), name=‘Input_9’)
BatchNormalization_3 = BatchNormalization(name=‘BatchNormalization_3’,axis= 1)(Input_9)
Convolution2D_9 = Convolution2D(name=‘Convolution2D_9’,nb_row= 3,border_mode= ‘same’ ,activation= ‘relu’ ,nb_col= 3,nb_filter= 28)(BatchNormalization_3)
Convolution2D_10 = Convolution2D(name=‘Convolution2D_10’,nb_row= 3,activation= ‘relu’ ,nb_col= 3,nb_filter= 28)(Convolution2D_9)
MaxPooling2D_5 = MaxPooling2D(name=‘MaxPooling2D_5’)(Convolution2D_10)
Dropout_7 = Dropout(name=‘Dropout_7’,p= 0.25)(MaxPooling2D_5)
Convolution2D_11 = Convolution2D(name=‘Convolution2D_11’,nb_row= 3,border_mode= ‘same’ ,activation= ‘relu’ ,nb_col= 3,nb_filter= 56)(Dropout_7)
Convolution2D_12 = Convolution2D(name=‘Convolution2D_12’,nb_row= 3,activation= ‘relu’ ,nb_col= 3,nb_filter= 56)(Convolution2D_11)
MaxPooling2D_6 = MaxPooling2D(name=‘MaxPooling2D_6’)(Convolution2D_12)
Dropout_8 = Dropout(name=‘Dropout_8’,p= 0.25)(MaxPooling2D_6)
Flatten_3 = Flatten(name=‘Flatten_3’)(Dropout_8)
Dense_5 = Dense(name=‘Dense_5’,output_dim= 128,activation= ‘relu’ )(Flatten_3)
Dropout_9 = Dropout(name=‘Dropout_9’,p= 0.5)(Dense_5)
Dense_6 = Dense(name=‘Dense_6’,output_dim= 15,activation= ‘softmax’ )(Dropout_9)
model = Model([Input_9],[Dense_6])
return aliases, model
from keras.optimizers import *
def get_optimizer():
return SGD(decay=0.0000001,nesterov=True,momentum=0.9)
def is_custom_loss_function():
return False
def get_loss_function():
return ‘categorical_crossentropy’
def get_batch_size():
return 32
def get_num_epoch():
return 10
def get_data_config():
return ‘{“mapping”: {“Rating”: {“port”: “OutputPort0”, “options”: {}, “shape”: “”, “type”: “Categorical”}, “Image”: {“port”: “InputPort0”, “options”: {“Augmentation”: false, “Scaling”: 1, “Normalization”: true, “Height”: 28, “height_shift_range”: 0, “Resize”: true, “width_shift_range”: 0, “Width”: “28”, “horizontal_flip”: false, “rotation_range”: 0, “pretrained”: “None”, “vertical_flip”: false, “shear_range”: 0}, “shape”: “”, “type”: “Image”}}, “dataset”: {“type”: “private”, “samples”: 3450, “name”: “ChampiDataset”}, “samples”: {“split”: 4, “training”: 2760, “validation”: 345, “test”: 345}, “datasetLoadOption”: “full”, “kfold”: 1, “numPorts”: 1, “shuffle”: true}’
Thank you
Lyranis