for layer in basemodel.layers:
layer.trainable = False
# compiling the model
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# training the nets
H = model.fit(aug.flow(trainX, trainY, batch_size=BS),
steps_per_epoch=len(trainX) // BS,
validation_data=(testX, testY),
validation_steps=len(testX) // BS,
epochs=EPOCHS)
# predictions on Test set
predictions = model.predict(testX, batch_size=BS)
# finding the index of the largest probability
predictions = np.argmax(predictions, axis=1)
# show a nicely formatted classification report
print(
classification_report(testY.argmax(axis=1),
predictions,
target_names=lb.classes_))
# saving the model
model.save(args["model"], save_format="h5")
# Plotting the training loss and accuaracy
N = EPOCHS
# Compile Model
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
'''
train model
'''
batch_size = 256
num_epochs = 20
# Train Model
history = model.fit(trainX, trainY, batch_size=batch_size,
epochs=num_epochs) #, callbacks=[checkpoint])
predY = model.predict(testX)
y_pred = np.argmax(predY, axis=1)
y_actual = np.argmax(testY, axis=1)
#y_label= [labels[k] for k in y_pred]
cm = confusion_matrix(y_actual, y_pred)
print(cm)
'''
confusion matrix
'''
import itertools
def plot_confusion_matrix(cm,
target_names,
title='Confusion matrix',
# for image in filenames:
# #print('../working/test1'+image)
# img = cv2.imread('../working/test1/'+image)
# img = cv2.resize(img,(224,224))
# img = img/255.0
# cls = model.predict(img.reshape(1,224,224,3)).argmax()
# if cls==0:
# category.append('cat')
# else:
# category.append('dog')
# file.append(image)
img = cv2.imread('../working/test1/101.jpg')
img = cv2.resize(img, (224, 224))
img = img / 255.0
model.predict(img.reshape(1, 224, 224, 3)).argmax()
import matplotlib.pyplot as plt
plt.imshow(img)
import pandas as pd
ser1 = pd.Series(data=file)
df_submit = pd.DataFrame(ser1, columns=['filename'])
df_submit['category'] = category
df_submit.head(5)
opt = Adam(lr=lr, decay=lr / epochs)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# train
print("[INFO] training model...")
H = model.fit(x=X_train,
y=y_train,
batch_size=batch_size,
steps_per_epoch=len(X_train) // batch_size,
validation_data=(X_test, y_test),
validation_steps=len(X_test) // batch_size,
epochs=epochs)
# test
print("[INFO] evaluating model...")
predIdxs = model.predict(X_test, batch_size=batch_size)
predIdxs = np.argmax(predIdxs, axis=1)
print(
classification_report(y_test.argmax(axis=1),
predIdxs,
target_names=lb.classes_))
# confusion matrix
cm = confusion_matrix(y_test.argmax(axis=1), predIdxs)
total = sum(sum(cm))
acc = (cm[0, 0] + cm[1, 1]) / total
sensitivity = cm[0, 0] / (cm[0, 0] + cm[0, 1])
specificity = cm[1, 1] / (cm[1, 0] + cm[1, 1])
print(cm)