Parcourir la source

Creation of modules for models

jbuisine il y a 5 ans
Parent
commit
67c2a0d3a5

+ 7 - 0
.gitignore

@@ -1,3 +1,10 @@
 # project data
 # project data
 data
 data
 .python-version
 .python-version
+__pycache__
+
+# by default avoid model files and png files
+*.h5
+*.png
+!saved_models/*.h5
+!saved_models/*.png

+ 15 - 0
README.md

@@ -17,11 +17,26 @@ It will split scenes and generate all data you need for your neural network.
 You can specify the number of sub images you want in the script by modifying NUMBER_SUB_IMAGES variables.
 You can specify the number of sub images you want in the script by modifying NUMBER_SUB_IMAGES variables.
 
 
 
 
+There are 3 kind of Neural Networks :
+- classification_cnn_keras.py : based croped on images
+- classification_cnn_keras_crossentropy.py : based croped on images which are randomly split for training
+- classification_cnn_keras_svd.py : based on svd metrics of image
+
+Note that the image input size need to change in you used specific size for your croped images.
+
 After your built your neural network in classification_cnn_keras.py, you just have to run it :
 After your built your neural network in classification_cnn_keras.py, you just have to run it :
 ```
 ```
 python classification_cnn_keras.py
 python classification_cnn_keras.py
 ```
 ```
 
 
+## Modules
+
+This project contains modules :
+- modules/image_metrics : where all computed metrics function are developed
+- modules/model_helper : contains helpful function to save or display model information and performance
+
+All these modules will grow during developement of the project
+
 ## How to contribute
 ## How to contribute
 
 
 This git project uses [git-flow](https://danielkummer.github.io/git-flow-cheatsheet/) implementation. You are free to contribute to it.
 This git project uses [git-flow](https://danielkummer.github.io/git-flow-cheatsheet/) implementation. You are free to contribute to it.

+ 147 - 65
classification_cnn_keras.py

@@ -23,12 +23,16 @@ data/
             ...
             ...
 ```
 ```
 '''
 '''
+import sys, os, getopt
 
 
 from keras.preprocessing.image import ImageDataGenerator
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.models import Sequential
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras import backend as K
 from keras import backend as K
+from keras.utils import plot_model
+
+from modules.model_helper import plot_info
 
 
 
 
 # dimensions of our images.
 # dimensions of our images.
@@ -46,68 +50,146 @@ if K.image_data_format() == 'channels_first':
 else:
 else:
     input_shape = (img_width, img_height, 3)
     input_shape = (img_width, img_height, 3)
 
 
-model = Sequential()
-model.add(Conv2D(60, (2, 2), input_shape=input_shape))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-
-model.add(Conv2D(40, (2, 2)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-
-model.add(Conv2D(20, (2, 2)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-
-model.add(Conv2D(10, (2, 2)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-
-model.add(Flatten())
-model.add(Dense(60))
-model.add(Activation('relu'))
-model.add(Dropout(0.4))
-
-model.add(Dense(30))
-model.add(Activation('relu'))
-model.add(Dropout(0.2))
-
-model.add(Dense(1))
-model.add(Activation('sigmoid'))
-
-model.compile(loss='binary_crossentropy',
-              optimizer='rmsprop',
-              metrics=['accuracy'])
-
-# this is the augmentation configuration we will use for training
-train_datagen = ImageDataGenerator(
-    rescale=1. / 255,
-    shear_range=0.2,
-    zoom_range=0.2,
-    horizontal_flip=True)
-
-# this is the augmentation configuration we will use for testing:
-# only rescaling
-test_datagen = ImageDataGenerator(rescale=1. / 255)
-
-train_generator = train_datagen.flow_from_directory(
-    train_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
-
-validation_generator = test_datagen.flow_from_directory(
-    validation_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
-
-model.summary()
-model.fit_generator(
-    train_generator,
-    steps_per_epoch=nb_train_samples // batch_size,
-    epochs=epochs,
-    validation_data=validation_generator,
-    validation_steps=nb_validation_samples // batch_size)
-
-model.save_weights('noise_classification_img100.h5')
+'''
+Method which returns model to train
+@return : DirectoryIterator
+'''
+def generate_model():
+
+    model = Sequential()
+    model.add(Conv2D(60, (2, 2), input_shape=input_shape))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Conv2D(40, (2, 2)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Conv2D(20, (2, 2)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Conv2D(10, (2, 2)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Flatten())
+    model.add(Dense(60))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.4))
+
+    model.add(Dense(30))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.2))
+
+    model.add(Dense(1))
+    model.add(Activation('sigmoid'))
+
+    model.compile(loss='binary_crossentropy',
+                  optimizer='rmsprop',
+                  metrics=['accuracy'])
+
+    return model
+
+'''
+Method which loads train data
+@return : DirectoryIterator
+'''
+def load_train_data():
+    # this is the augmentation configuration we will use for training
+    train_datagen = ImageDataGenerator(
+        rescale=1. / 255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    return train_generator
+
+'''
+Method which loads validation data
+@return : DirectoryIterator
+'''
+def load_validation_data():
+
+    # this is the augmentation configuration we will use for testing:
+    # only rescaling
+    test_datagen = ImageDataGenerator(rescale=1. / 255)
+
+    validation_generator = test_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    return validation_generator
+
+def main():
+
+    global batch_size
+    global epochs
+
+    if len(sys.argv) <= 1:
+        print('No output file defined...')
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "ho:b:e:d", ["help", "directory=", "output=", "batch_size=", "epochs="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            print('classification_cnn_keras_svd.py --output xxxxx')
+            sys.exit()
+        elif o in ("-o", "--output"):
+            filename = a
+        elif o in ("-b", "--batch_size"):
+            batch_size = int(a)
+        elif o in ("-e", "--epochs"):
+            epochs = int(a)
+        elif o in ("-d", "--directory"):
+            directory = a
+        else:
+            assert False, "unhandled option"
+
+
+    # load of model
+    model = generate_model()
+    model.summary()
+
+    if(directory):
+        print('Your model information will be saved into %s...' % directory)
+
+    history = model.fit_generator(
+        load_train_data(),
+        steps_per_epoch=nb_train_samples // batch_size,
+        epochs=epochs,
+        validation_data=load_validation_data(),
+        validation_steps=nb_validation_samples // batch_size)
+
+    # if user needs output files
+    if(filename):
+
+        # update filename by folder
+        if(directory):
+            # create folder if necessary
+            if not os.path.exists(directory):
+                os.makedirs(directory)
+            filename = directory + "/" + filename
+
+        # save plot file history
+        plot_info.save(history, filename)
+
+        plot_model(model, to_file=str(('%s.png' % filename)))
+        model.save_weights(str('%s.h5' % filename))
+
+
+if __name__ == "__main__":
+    main()

+ 66 - 19
classification_cnn_keras_cross_validation.py

@@ -23,15 +23,16 @@ data/
             ...
             ...
 ```
 ```
 '''
 '''
+import sys, os, getopt
 
 
 from keras.preprocessing.image import ImageDataGenerator
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.models import Sequential
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras import backend as K
 from keras import backend as K
-from sklearn.cross_validation import StratifiedKFold
 from keras.utils import plot_model
 from keras.utils import plot_model
 
 
+from modules.model_helper import plot_info
 
 
 # dimensions of our images.
 # dimensions of our images.
 img_width, img_height = 100, 100
 img_width, img_height = 100, 100
@@ -50,7 +51,11 @@ else:
 
 
 
 
 
 
-def create_model():
+'''
+Method which returns model to train
+@return : DirectoryIterator
+'''
+def generate_model():
     # create your model using this function
     # create your model using this function
     model = Sequential()
     model = Sequential()
     model.add(Conv2D(60, (2, 2), input_shape=input_shape))
     model.add(Conv2D(60, (2, 2), input_shape=input_shape))
@@ -98,8 +103,6 @@ def create_model():
                   optimizer='rmsprop',
                   optimizer='rmsprop',
                   metrics=['accuracy'])
                   metrics=['accuracy'])
 
 
-    model.summary()
-    plot_model(model, to_file='noise_classification_img100.png', show_shapes=True)
     return model
     return model
 
 
 def load_data():
 def load_data():
@@ -111,10 +114,6 @@ def load_data():
         zoom_range=0.2,
         zoom_range=0.2,
         horizontal_flip=True)
         horizontal_flip=True)
 
 
-    # this is the augmentation configuration we will use for testing:
-    # only rescaling
-    test_datagen = ImageDataGenerator(rescale=1. / 255)
-
     train_generator = train_datagen.flow_from_directory(
     train_generator = train_datagen.flow_from_directory(
         train_data_dir,
         train_data_dir,
         target_size=(img_width, img_height),
         target_size=(img_width, img_height),
@@ -123,15 +122,9 @@ def load_data():
 
 
     return train_generator
     return train_generator
 
 
-    #validation_generator = test_datagen.flow_from_directory(
-    #    validation_data_dir,
-    #    target_size=(img_width, img_height),
-    #    batch_size=batch_size,
-    #    class_mode='binary')
-
 def train_and_evaluate_model(model, data_train, data_test):
 def train_and_evaluate_model(model, data_train, data_test):
 
 
-    model.fit_generator(
+    return model.fit_generator(
         data_train,
         data_train,
         steps_per_epoch=nb_train_samples // batch_size,
         steps_per_epoch=nb_train_samples // batch_size,
         epochs=epochs,
         epochs=epochs,
@@ -139,7 +132,41 @@ def train_and_evaluate_model(model, data_train, data_test):
         validation_data=data_test,
         validation_data=data_test,
         validation_steps=nb_validation_samples // batch_size)
         validation_steps=nb_validation_samples // batch_size)
 
 
-if __name__ == "__main__":
+def main():
+
+    global batch_size
+    global epochs
+
+    if len(sys.argv) <= 1:
+        print('No output file defined...')
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "ho:b:e:d", ["help", "directory=", "output=", "batch_size=", "epochs="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            print('classification_cnn_keras_svd.py --output xxxxx')
+            sys.exit()
+        elif o in ("-o", "--output"):
+            filename = a
+        elif o in ("-b", "--batch_size"):
+            batch_size = int(a)
+        elif o in ("-e", "--epochs"):
+            epochs = int(a)
+        elif o in ("-d", "--directory"):
+            directory = a
+        else:
+            assert False, "unhandled option"
+
+
+    # load of model
+    model = generate_model()
+    model.summary()
+
     n_folds = 10
     n_folds = 10
 
 
     data_generator = ImageDataGenerator(rescale=1./255, validation_split=0.33)
     data_generator = ImageDataGenerator(rescale=1./255, validation_split=0.33)
@@ -151,7 +178,27 @@ if __name__ == "__main__":
     validation_generator = data_generator.flow_from_directory(train_data_dir, target_size=(img_width, img_height), shuffle=True, seed=13,
     validation_generator = data_generator.flow_from_directory(train_data_dir, target_size=(img_width, img_height), shuffle=True, seed=13,
                                                          class_mode='binary', batch_size=batch_size, subset="validation")
                                                          class_mode='binary', batch_size=batch_size, subset="validation")
 
 
-    model = create_model()
-    train_and_evaluate_model(model, train_generator, validation_generator)
+    # now run model
+    history = train_and_evaluate_model(model, train_generator, validation_generator)
+
+    print("directory %s " % directory)
+    if(directory):
+        print('Your model information will be saved into %s...' % directory)
+    # if user needs output files
+    if(filename):
+
+        # update filename by folder
+        if(directory):
+            # create folder if necessary
+            if not os.path.exists(directory):
+                os.makedirs(directory)
+            filename = directory + "/" + filename
 
 
-    model.save_weights('noise_classification_img100.h5')
+        # save plot file history
+        plot_info.save(history, filename)
+
+        plot_model(model, to_file=str(('%s.png' % filename)))
+        model.save_weights(str('%s.h5' % filename))
+
+if __name__ == "__main__":
+    main()

+ 166 - 95
classification_cnn_keras_svd.py

@@ -23,6 +23,7 @@ data/
             ...
             ...
 ```
 ```
 '''
 '''
+import sys, os, getopt
 
 
 from keras.preprocessing.image import ImageDataGenerator
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.models import Sequential
@@ -31,17 +32,20 @@ from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
 from keras.optimizers import Adam
 from keras.optimizers import Adam
 from keras.regularizers import l2
 from keras.regularizers import l2
 from keras import backend as K
 from keras import backend as K
-from numpy.linalg import svd
+from keras.utils import plot_model
+
+import matplotlib.pyplot as plt
+
 import tensorflow as tf
 import tensorflow as tf
 import numpy as np
 import numpy as np
-from PIL import Image
 
 
-from scipy import misc
-import matplotlib.pyplot as plt
-import keras as k
+from modules.model_helper import plot_info
+from modules.image_metrics import svd_metric
 
 
+
+# configuration
 # dimensions of our images.
 # dimensions of our images.
-img_width, img_height = int(100), 1
+img_width, img_height = 100, 1
 
 
 train_data_dir = 'data/train'
 train_data_dir = 'data/train'
 validation_data_dir = 'data/validation'
 validation_data_dir = 'data/validation'
@@ -50,97 +54,164 @@ nb_validation_samples = 3600
 epochs = 200
 epochs = 200
 batch_size = 30
 batch_size = 30
 
 
-# configuration
-config = tf.ConfigProto(intra_op_parallelism_threads=6, inter_op_parallelism_threads=6, \
-                        allow_soft_placement=True, device_count = {'CPU': 6})
-session = tf.Session(config=config)
-K.set_session(session)
-
-def svd_singular(image):
-    U, s, V = svd(image, full_matrices=False)
-    s = s[0:img_width]
-    result = s.reshape([img_width, 1, 1]) # one shape per canal
-    return result
-
 if K.image_data_format() == 'channels_first':
 if K.image_data_format() == 'channels_first':
     input_shape = (3, img_width, img_height)
     input_shape = (3, img_width, img_height)
 else:
 else:
     input_shape = (img_width, img_height, 3)
     input_shape = (img_width, img_height, 3)
 
 
-model = Sequential()
-
-model.add(Conv2D(100, (2, 1), input_shape=input_shape))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 1)))
-
-model.add(Conv2D(80, (2, 1)))
-model.add(Activation('relu'))
-model.add(AveragePooling2D(pool_size=(2, 1)))
-
-model.add(Conv2D(50, (2, 1)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 1)))
-
-model.add(Flatten())
-model.add(BatchNormalization())
-model.add(Dense(300, kernel_regularizer=l2(0.01)))
-model.add(Activation('relu'))
-model.add(Dropout(0.4))
-
-model.add(Dense(30, kernel_regularizer=l2(0.01)))
-model.add(BatchNormalization())
-model.add(Activation('relu'))
-model.add(Dropout(0.3))
-
-model.add(Dense(100, kernel_regularizer=l2(0.01)))
-model.add(BatchNormalization())
-model.add(Activation('relu'))
-model.add(Dropout(0.2))
-
-model.add(Dense(20, kernel_regularizer=l2(0.01)))
-model.add(BatchNormalization())
-model.add(Activation('relu'))
-model.add(Dropout(0.1))
-
-model.add(Dense(1))
-model.add(Activation('sigmoid'))
-
-model.compile(loss='binary_crossentropy',
-              optimizer='rmsprop',
-              metrics=['accuracy'])
-
-# this is the augmentation configuration we will use for training
-train_datagen = ImageDataGenerator(
-    #rescale=1. / 255,
-    #shear_range=0.2,
-    #zoom_range=0.2,
-    #horizontal_flip=True,
-    preprocessing_function=svd_singular)
-# this is the augmentation configuration we will use for testing:
-# only rescaling
-test_datagen = ImageDataGenerator(
-    #rescale=1. / 255,
-    preprocessing_function=svd_singular)
-
-train_generator = train_datagen.flow_from_directory(
-    train_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
-
-validation_generator = test_datagen.flow_from_directory(
-    validation_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
-
-
-model.summary()
-model.fit_generator(
-    train_generator,
-    steps_per_epoch=nb_train_samples // batch_size,
-    epochs=epochs,
-    validation_data=validation_generator,
-    validation_steps=nb_validation_samples // batch_size)
-
-model.save_weights('noise_classification_img100.h5')
+'''
+Method which returns model to train
+@return : DirectoryIterator
+'''
+def generate_model():
+
+    model = Sequential()
+
+    model.add(Conv2D(100, (2, 1), input_shape=input_shape))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 1)))
+
+    model.add(Conv2D(80, (2, 1)))
+    model.add(Activation('relu'))
+    model.add(AveragePooling2D(pool_size=(2, 1)))
+
+    model.add(Conv2D(50, (2, 1)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 1)))
+
+    model.add(Flatten())
+    model.add(BatchNormalization())
+    model.add(Dense(300, kernel_regularizer=l2(0.01)))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.4))
+
+    model.add(Dense(30, kernel_regularizer=l2(0.01)))
+    model.add(BatchNormalization())
+    model.add(Activation('relu'))
+    model.add(Dropout(0.3))
+
+    model.add(Dense(100, kernel_regularizer=l2(0.01)))
+    model.add(BatchNormalization())
+    model.add(Activation('relu'))
+    model.add(Dropout(0.2))
+
+    model.add(Dense(20, kernel_regularizer=l2(0.01)))
+    model.add(BatchNormalization())
+    model.add(Activation('relu'))
+    model.add(Dropout(0.1))
+
+    model.add(Dense(1))
+    model.add(Activation('sigmoid'))
+
+    model.compile(loss='binary_crossentropy',
+                  optimizer='rmsprop',
+                  metrics=['accuracy'])
+
+    return model
+
+'''
+Method which loads train data
+@return : DirectoryIterator
+'''
+def load_train_data():
+
+    # this is the augmentation configuration we will use for training
+    train_datagen = ImageDataGenerator(
+        rescale=1. / 255,
+        #shear_range=0.2,
+        #zoom_range=0.2,
+        #horizontal_flip=True,
+        preprocessing_function=svd_metric.get_s_model_data)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    return train_generator
+
+'''
+Method which loads validation data
+@return : DirectoryIterator
+'''
+def load_validation_data():
+
+    # this is the augmentation configuration we will use for testing:
+    # only rescaling
+    test_datagen = ImageDataGenerator(
+        rescale=1. / 255,
+        preprocessing_function=svd_metric.get_s_model_data)
+
+    validation_generator = test_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    return validation_generator
+
+def main():
+
+    global batch_size
+    global epochs
+
+    if len(sys.argv) <= 1:
+        print('No output file defined...')
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "ho:b:e:d", ["help", "directory=", "output=", "batch_size=", "epochs="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        print('classification_cnn_keras_svd.py --output xxxxx')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            print('classification_cnn_keras_svd.py --output xxxxx')
+            sys.exit()
+        elif o in ("-o", "--output"):
+            filename = a
+        elif o in ("-b", "--batch_size"):
+            batch_size = int(a)
+        elif o in ("-e", "--epochs"):
+            epochs = int(a)
+        elif o in ("-d", "--directory"):
+            directory = a
+        else:
+            assert False, "unhandled option"
+
+
+    # load of model
+    model = generate_model()
+    model.summary()
+
+    if(directory):
+        print('Your model information will be saved into %s...' % directory)
+
+    history = model.fit_generator(
+        load_train_data(),
+        steps_per_epoch=nb_train_samples // batch_size,
+        epochs=epochs,
+        validation_data=load_validation_data(),
+        validation_steps=nb_validation_samples // batch_size)
+
+    # if user needs output files
+    if(filename):
+
+        # update filename by folder
+        if(directory):
+            # create folder if necessary
+            if not os.path.exists(directory):
+                os.makedirs(directory)
+            filename = directory + "/" + filename
+
+        # save plot file history
+        plot_info.save(history, filename)
+
+        plot_model(model, to_file=str(('%s.png' % filename)))
+        model.save_weights(str('%s.h5' % filename))
+
+
+if __name__ == "__main__":
+    main()

+ 0 - 0
modules/__init__.py


+ 0 - 0
modules/image_metrics/__init__.py


+ 30 - 0
modules/image_metrics/svd_metric.py

@@ -0,0 +1,30 @@
+# module file which contains all image metrics used in project
+
+from numpy.linalg import svd
+from PIL import Image
+from scipy import misc
+
+'''
+Method which extracts SVD features from image and returns 's' vector
+@return 's' vector
+'''
+def get_s_model_data(image):
+    U, s, V = svd(image, full_matrices=False)
+    size = len(s)
+    result = s.reshape([size, 1, 1]) # one shape per canal
+    return result
+
+def get(image):
+    return svd(image, full_matrices=False)
+
+def get_s(image):
+    U, s, V = svd(image, full_matrices=False)
+    return s
+
+def get_U(image):
+    U, s, V = svd(image, full_matrices=False)
+    return U
+
+def get_V(image):
+    U, s, V = svd(image, full_matrices=False)
+    return V

+ 0 - 0
modules/model_helper/__init__.py


+ 47 - 0
modules/model_helper/plot_info.py

@@ -0,0 +1,47 @@
+# module filewhich contains helpful display function
+
+import matplotlib.pyplot as plt
+
+'''
+Function which saves data from neural network model
+'''
+def save(history, filename):
+    # summarize history for accuracy
+    plt.plot(history.history['acc'])
+    plt.plot(history.history['val_acc'])
+    plt.title('model accuracy')
+    plt.ylabel('accuracy')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    plt.savefig(str('%s_accuracy.png' % filename))
+
+    # clear plt history
+    plt.gcf().clear()
+
+    # summarize history for loss
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title('model loss')
+    plt.ylabel('loss')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    plt.savefig(str('%s_loss.png' % filename))
+
+def show(history, filename):
+    # summarize history for accuracy
+    plt.plot(history.history['acc'])
+    plt.plot(history.history['val_acc'])
+    plt.title('model accuracy')
+    plt.ylabel('accuracy')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    plt.show()
+
+    # summarize history for loss
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title('model loss')
+    plt.ylabel('loss')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    plt.show()