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DataAumgentationTrain.txt
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DataAumgentationTrain.txt
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from keras.models import Sequential, Model
from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout, BatchNormalization, Input
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.utils import np_utils
import os
import numpy as np
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.applications.vgg16 import VGG16
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
from keras.preprocessing.image import ImageDataGenerator
width_shape = 224
height_shape = 224
num_classes = 10
epochs = 50
batch_size = 32
train_data_dir = 'D:/Video Tutoriales/ImageClassification/dataset/train'
validation_data_dir = 'D:/Video Tutoriales/ImageClassification/dataset/valid'
train_datagen = ImageDataGenerator(
rotation_range=20,
zoom_range=0.2,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=False,
preprocessing_function=preprocess_input)
valid_datagen = ImageDataGenerator(
rotation_range=20,
zoom_range=0.2,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=False,
preprocessing_function=preprocess_input)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(width_shape, height_shape),
batch_size=batch_size,
#save_to_dir='',
class_mode='categorical')
validation_generator = valid_datagen.flow_from_directory(
validation_data_dir,
target_size=(width_shape, height_shape),
batch_size=batch_size,
#save_to_dir='',
class_mode='categorical')
#Creación de modelo
nb_train_samples = 1490
nb_validation_samples = 50
model = Sequential()
inputShape = (height_shape, width_shape, 3)
model.add(Conv2D(32,(3,3), input_shape=inputShape))
model.add(Conv2D(32,(3,3)))
model.add(MaxPool2D())
model.add(Conv2D(64,(3,3)))
model.add(Conv2D(64,(3,3)))
model.add(Conv2D(64,(3,3)))
model.add(MaxPool2D())
#model.add(Conv2D(128,(3,3)))
#model.add(Conv2D(128,(3,3)))
#model.add(Conv2D(128,(3,3)))
#model.add(MaxPool2D())
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dense(32,activation='relu'))
model.add(Dense(num_classes,activation='softmax', name='output'))
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
model_history = model.fit_generator(
train_generator,
epochs=epochs,
validation_data=validation_generator,
steps_per_epoch=nb_train_samples//batch_size,
validation_steps=nb_validation_samples//batch_size)
# Modelo VGG16
nb_train_samples = 1490
nb_validation_samples = 50
image_input = Input(shape=(width_shape, height_shape, 3))
model = VGG16(input_tensor=image_input, include_top=True,weights='imagenet')
last_layer = model.get_layer('fc2').output
out = Dense(num_classes, activation='softmax', name='output')(last_layer)
custom_vgg_model = Model(image_input, out)
for layer in custom_vgg_model.layers[:-1]:
layer.trainable = False
custom_vgg_model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
custom_vgg_model.summary()
model_history = custom_vgg_model.fit_generator(
train_generator,
epochs=epochs,
validation_data=validation_generator,
steps_per_epoch=nb_train_samples//batch_size,
validation_steps=nb_validation_samples//batch_size)
custom_vgg_model.save("model_VGG16.h5")
#Plot
def plotTraining(hist, epochs, typeData):
if typeData=="loss":
plt.figure(1,figsize=(10,5))
yc=hist.history['loss']
xc=range(epochs)
plt.ylabel('Loss', fontsize=24)
plt.plot(xc,yc,'-r',label='Loss Training')
if typeData=="accuracy":
plt.figure(2,figsize=(10,5))
yc=hist.history['accuracy']
for i in range(0, len(yc)):
yc[i]=100*yc[i]
xc=range(epochs)
plt.ylabel('Accuracy (%)', fontsize=24)
plt.plot(xc,yc,'-r',label='Accuracy Training')
if typeData=="val_loss":
plt.figure(1,figsize=(10,5))
yc=hist.history['val_loss']
xc=range(epochs)
plt.ylabel('Loss', fontsize=24)
plt.plot(xc,yc,'--b',label='Loss Validate')
if typeData=="val_accuracy":
plt.figure(2,figsize=(10,5))
yc=hist.history['val_accuracy']
for i in range(0, len(yc)):
yc[i]=100*yc[i]
xc=range(epochs)
plt.ylabel('Accuracy (%)', fontsize=24)
plt.plot(xc,yc,'--b',label='Training Validate')
plt.rc('xtick',labelsize=24)
plt.rc('ytick',labelsize=24)
plt.rc('legend', fontsize=18)
plt.legend()
plt.xlabel('Number of Epochs',fontsize=24)
plt.grid(True)
# Plot history
plotTraining(model_history,epochs,"loss")
plotTraining(model_history,epochs,"accuracy")
plotTraining(model_history,epochs,"val_loss")
plotTraining(model_history,epochs,"val_accuracy")
# Prediction
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
names = ['AFRICAN FIREFINCH','ALBATROSS','ALEXANDRINE PARAKEET','AMERICAN AVOCET','AMERICAN BITTERN',
'AMERICAN COOT','AMERICAN GOLDFINCH','AMERICAN KESTREL','AMERICAN PIPIT','AMERICAN REDSTART']
modelt= custom_vgg_model
imaget_path = "D:/Video Tutoriales/ImageClassification/dataset/test/ALEXANDRINE PARAKEET/1.jpg"
imaget=cv2.resize(cv2.imread(imaget_path), (width_shape, height_shape), interpolation = cv2.INTER_AREA)
xt = np.asarray(imaget)
xt=preprocess_input(xt)
xt = np.expand_dims(xt,axis=0)
preds = modelt.predict(xt)
print(names[np.argmax(preds)])
plt.imshow(cv2.cvtColor(np.asarray(imaget),cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()
from sklearn.metrics import confusion_matrix, f1_score, roc_curve, precision_score, recall_score, accuracy_score, roc_auc_score
from sklearn import metrics
from mlxtend.plotting import plot_confusion_matrix
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
names = ['AFRICAN FIREFINCH','ALBATROSS','ALEXANDRINE PARAKEET','AMERICAN AVOCET','AMERICAN BITTERN',
'AMERICAN COOT','AMERICAN GOLDFINCH','AMERICAN KESTREL','AMERICAN PIPIT','AMERICAN REDSTART']
test_data_dir = 'D:/Video Tutoriales/ImageClassification/dataset/test'
test_datagen = ImageDataGenerator()
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(width_shape, height_shape),
batch_size = batch_size,
class_mode='categorical',
shuffle=False)
custom_Model= load_model("model_VGG16.h5")
predictions = custom_Model.predict_generator(generator=test_generator)
y_pred = np.argmax(predictions, axis=1)
y_real = test_generator.classes
matc=confusion_matrix(y_real, y_pred)
plot_confusion_matrix(conf_mat=matc, figsize=(9,9), class_names = names, show_normed=False)
plt.tight_layout()
print(metrics.classification_report(y_real,y_pred, digits = 4))