Solution
Open your Google Colab interface.
Note
You will need to mount your drive using the Dataset folder, and accordingly set the path to continue ahead.
import numpyasnp classes=['daisy','dandelion','rose','sunflower','tulip'] X=np.load("Dataset/flowers/%s_x.npy"%(classes[0])) y=np.load("Dataset/flowers/%s_y.npy"%(classes[0])) print(X.shape) forflowerinclasses[1:]: X_aux=np.load("Dataset/flowers/%s_x.npy"%(flower)) y_aux=np.load("Dataset/flowers/%s_y.npy"%(flower)) print(X_aux.shape) X=np.concatenate((X,X_aux),axis=0) y=np.concatenate((y,y_aux),axis=0) print(X.shape) print(y.shape)To output some samples from the dataset:
import random random.seed(42) from matplotlib import pyplot as plt import cv2 for idx in range(5): rnd_index = random.randint(0, 4000) plt.subplot(1,5,idx+1),plt.imshow(cv2.cvtColor(X[rnd_index],cv2.COLOR_BGR2RGB)) plt.xticks([]),plt.yticks([]) plt.savefig("flowers_samples.jpg", bbox_inches='tight') plt.show()The output is as follows:
Figure 5.23: Samples from the dataset
Now, we will normalize and perform one-hot encoding:
from keras import utils as np_utils X = (X.astype(np.float32))/255.0 y = np_utils.to_categorical(y, len(classes)) print(X.shape) print(y.shape)
Splitting the training and testing set:
from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2) input_shape = x_train.shape[1:] print(x_train.shape) print(y_train.shape) print(x_test.shape) print(y_test.shape) print(input_shape)
Import libraries and build the CNN:
from keras.models import Sequential from keras.callbacks import ModelCheckpoint from keras.layers import Input, Dense, Dropout, Flatten from keras.layers import Conv2D, Activation, BatchNormalization def CNN(input_shape): model = Sequential() model.add(Conv2D(32, kernel_size=(5, 5), padding='same', strides=(2,2), input_shape=input_shape)) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.2)) model.add(Conv2D(64, kernel_size=(3, 3), padding='same', strides=(2,2))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.2)) model.add(Conv2D(128, kernel_size=(3, 3), padding='same', strides=(2,2))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.2)) model.add(Conv2D(256, kernel_size=(3, 3), padding='same', strides=(2,2))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.2)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(5, activation = "softmax")) return modelDeclare ImageDataGenerator:
from keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( rotation_range=10, zoom_range = 0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.1, horizontal_flip=True )
We will now train the model:
datagen.fit(x_train) model = CNN(input_shape) model.compile(loss='categorical_crossentropy', optimizer='Adadelta', metrics=['accuracy']) ckpt = ModelCheckpoint('Models/model_flowers.h5', save_best_only=True,monitor='val_loss', mode='min', save_weights_only=False) //{…}##the detailed code can be found on Github## model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), epochs=200, validation_data=(x_test, y_test), callbacks=[ckpt], steps_per_epoch=len(x_train) // 32, workers=4)After which, we will evaluate the model:
from sklearn import metrics model.load_weights('Models/model_flowers.h5') y_pred = model.predict(x_test, batch_size=32, verbose=0) y_pred = np.argmax(y_pred, axis=1) y_test_aux = y_test.copy() y_test_pred = list() for i in y_test_aux: y_test_pred.append(np.argmax(i)) //{…} ##the detailed code can be found on Github## print (y_pred) # Evaluate the prediction accuracy = metrics.accuracy_score(y_test_pred, y_pred) print('Acc: %.4f' % accuracy)The accuracy achieved is 91.68%.
Try the model with unseen data:
classes = ['daisy','dandelion','rose','sunflower','tulip'] images = ['sunflower.jpg','daisy.jpg','rose.jpg','dandelion.jpg','tulip .jpg'] model.load_weights('Models/model_flowers.h5') for number in range(len(images)): imgLoaded = cv2.imread('Dataset/testing/%s'%(images[number])) img = cv2.resize(imgLoaded, (150, 150)) img = (img.astype(np.float32))/255.0 img = img.reshape(1, 150, 150, 3) plt.subplot(1,5,number+1),plt.imshow(cv2.cvtColor(imgLoaded,cv2.COLOR_BGR2RGB)) plt.title(np.argmax(model.predict(img)[0])) plt.xticks([]),plt.yticks([]) plt.show()Output will look like this:
Figure 5.24: Prediction of roses result from Activity05