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CNN_train.py
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CNN_train.py
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#Code to build the model and train it
from keras.utils import plot_model
from keras.models import Sequential
from keras.layers import Convolution2D, Dropout, Dense, Flatten, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator, load_img
from numpy import array
from keras import regularizers
import cv2
#initializing the model
model= Sequential()
#adding convolutional layers and pooling layers
model.add(Convolution2D(32,3,3, input_shape=(45,45,1),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Convolution2D(32,3,3, input_shape=(45,45,1),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#Dropout layers to reduce over-fitting
model.add(Dropout(0.3))
model.add(Flatten())
#Now two hidden(dense) or fully-connected layers are added:
model.add(Dense(output_dim = 300, activation = 'relu',
kernel_regularizer=regularizers.l2(0.01)))
model.add(Dropout(0.4))#again for regularization
model.add(Dense(output_dim = 300, activation = 'relu',
kernel_regularizer=regularizers.l2(0.01)))
model.add(Dropout(0.3))#last one lol
model.add(Dense(output_dim = 150, activation = 'relu',
kernel_regularizer=regularizers.l2(0.01)))
#output layer
model.add(Dense(output_dim = 24, activation = 'sigmoid'))
#Now compile it
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#Now generate training and test sets from folders
train_datagen=ImageDataGenerator(
rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.,
horizontal_flip = False,
validation_split=0.2
)
training_set=train_datagen.flow_from_directory("dataset",
target_size = (45,45),
color_mode='grayscale',
batch_size=10,
class_mode='categorical',subset='training')
test_set=train_datagen.flow_from_directory("dataset",
target_size = (45,45),
color_mode='grayscale',
batch_size=10,
class_mode='categorical',subset='validation')
#finally, start training
model.fit_generator(training_set,
samples_per_epoch = 1956,
nb_epoch = 80,
validation_data = test_set,
nb_val_samples = 320)
#saving the weights
model.save_weights("weights.hdf5",overwrite=True)
#saving the model itself in json format:
model_json = model.to_json()
with open("model.json", "w") as model_file:
model_file.write(model_json)
print("Model has been saved.")