training and overall keras code complete

Author: CodeRex7

CNN_deeplearning.py

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+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras import backend as K
+import cv2
+import numpy as np
+
+
+# dimensions of our images.
+img_width, img_height = 150, 150
+
+# Path of the trainng folder
+train_data_dir = 'data/train'
+# Path of the validation folder
+validation_data_dir = 'data/validation'
+# set it according to no of images in the train directory
+nb_train_samples = 210
+# set it according to no of images in the validation directory
+nb_validation_samples = 50
+epochs = 200
+batch_size = 10
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+# CNN model design
+model = Sequential()
+model.add(Conv2D(32, (3, 3), input_shape=input_shape))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(32, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+model.add(Dense(64))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(9, activation='softmax'))
+#model.add(Activation('sigmoid'))
+
+model.compile(loss='categorical_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='categorical')
+
+#print (class_indices)
+validation_generator = test_datagen.flow_from_directory(
+    validation_data_dir,
+    target_size=(img_width, img_height),
+    batch_size=batch_size,
+    class_mode='categorical')
+print (validation_generator.class_indices)
+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)
+
+# Set the path of the image to be classified
+img = cv2.imread('map_12.jpg')
+img = cv2.resize(img,(150,150))
+img = np.reshape(img,[1,150,150,3])
+
+
+cla = model.predict_classes(img)
+print (cla[0])
+
+
+if(cla[0] == 1):
+    print ('This image can be described as a bar chart which represents data with rectangular bars with heights proportional to the values that they represent.')
+elif(cla[0] == 2):
+    print ('This image is a graph of a function. It has x and y axes with curves and lines depicting a function.')
+elif(cla[0] == 3):
+    print ('This image has a geometrical figure.')
+elif(cla[0] == 4):
+    print ('This image is a line graph in which information is displayed as a series of data points connected by straight line segments.')
+elif(cla[0] == 5):
+    print ('This image is a mapping of function which shows the relations of inputs and output in form of ordered pair. ')
+elif(cla[0] == 6):
+	print ('This image doesnot belong to any mathematical representation or figures.')                                                                                   
+elif(cla[0] == 7):
+	print ('This image is a pie chart, a circle which is divided into sectors that each represent a proportion of the whole.')
+elif(cla[0] == 8):
+	print ('This image is a graph with x and y axes where points are plotted and drawn.')
+elif(cla[0] == 0):
+	print ('This image is a venn diagram in which logical sets represented as circles or closed curves within an enclosing rectangle,the universal set.')
+
+model.save('model1.h5')

CNN_predict.py

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+from keras.models import load_model
+import cv2
+import numpy as np
+
+model = load_model('model1.h5')
+
+model.compile(loss='categorical_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+# Set the path of the image to be classified
+img = cv2.imread('graph_2.jpg')
+img = cv2.resize(img,(150,150))
+img = np.reshape(img,[1,150,150,3])
+
+cla = model.predict_classes(img)
+print (cla[0])
+if(cla[0] == 1):
+    print ('This image can be described as a bar chart which represents data with rectangular bars with heights proportional to the values that they represent.')
+elif(cla[0] == 2):
+    print ('This image is a graph of a function. It has x and y axes with curves and lines depicting a function.')
+elif(cla[0] == 3):
+    print ('This image has a geometrical figure.')
+elif(cla[0] == 4):
+    print ('This image is a line graph in which information is displayed as a series of data points connected by straight line segments.')
+elif(cla[0] == 5):
+    print ('This image is a mapping of function which shows the relations of inputs and output in form of ordered pair. ')
+elif(cla[0] == 6):
+	print ('This image doesnot belong to any mathematical representation or figures.')                                                                                   
+elif(cla[0] == 7):
+	print ('This image is a pie chart, a circle which is divided into sectors that each represent a proportion of the whole.')
+elif(cla[0] == 8):
+	print ('This image is a graph with x and y axes where points are plotted and drawn.')
+elif(cla[0] == 0):
+	print ('This image is a venn diagram in which logical sets represented as circles or closed curves within an enclosing rectangle,the universal set.')
+
+