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How to let predict_generator make ordered prediction in deep learning?
1
I am designing a deep learning model to classify images and I am using the following code to check the predication performance. It compares the images' labels with the predicted classes and then returns the error predication.
I was using sequential model, and this code was working fine. But, now it does not work fine.. predict_generator changes the order of the images. (predict_classes and validation_generator are not in the same order)
I want to make the prediction in the same order of the images in validation_generator, Do you know how can I do that??
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(image_size, image_size),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=False)
# Get the filenames from the generator
fnames = validation_generator.filenames
# Get the ground truth from generator
ground_truth = validation_generator.classes
# Get the label to class mapping from the generator
label2index = validation_generator.class_indices
# Getting the mapping from class index to class label
idx2label = dict((v,k) for k,v in label2index.items())
# Get the predictions from the model using the generator
predictions = model.predict_generator(validation_generator, steps=validation_generator.samples/validation_generator.batch_size,verbose=1)
predicted_classes = np.argmax(predictions,axis=1)
errors = np.where(predicted_classes != ground_truth)[0]
print("No of errors = {}/{}".format(len(errors),validation_generator.samples))
# Show the errors
for i in range(len(errors)):
pred_class = np.argmax(predictions[errors[i]])
pred_label = idx2label[pred_class]
title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(
fnames[errors[i]].split('/')[0],
pred_label,
predictions[errors[i]][pred_class])
original = load_img('{}/{}'.format(validation_dir,fnames[errors[i]]))
plt.figure(figsize=[7,7])
plt.axis('off')
plt.title(title)
plt.imshow(original)
plt.show()
The model code
from keras.engine import Model
vgg16_model = keras.applications.vgg16.VGG16()
input_layer2 = vgg16_model.input
vgg16_model.get_layer(index = 0).name = 'input2'
last_layer2 = vgg16_model.get_layer('block4_pool').output
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv1', padding='same')(last_layer2)
x = BatchNormalization(name='bn1')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv2', padding='same')(x)
x = BatchNormalization(name='bn2')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv3', padding='same')(x)
x = BatchNormalization(name='bn3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name = 'block5_pool')(x)
x = Flatten(name = 'flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
out2 = Dropout(0.3, name='dropout1')(x)
#Classification layer
output_layer2 = Dense(class_no, activation='softmax', name='prediction')(out2)
vgg16_face_model = Model(input_layer2, output_layer2)
vgg16_face_model.trainable = True
set_trainable = False
for layer in vgg16_face_model.layers:
if layer.name == 'block5_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
Please Help me..
python tensorflow machine-learning keras neural-network
asked Nov 21 '18 at 21:59
NoranNoran
1999
add a comment |
1
I am designing a deep learning model to classify images and I am using the following code to check the predication performance. It compares the images' labels with the predicted classes and then returns the error predication.
I was using sequential model, and this code was working fine. But, now it does not work fine.. predict_generator changes the order of the images. (predict_classes and validation_generator are not in the same order)
I want to make the prediction in the same order of the images in validation_generator, Do you know how can I do that??
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(image_size, image_size),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=False)
# Get the filenames from the generator
fnames = validation_generator.filenames
# Get the ground truth from generator
ground_truth = validation_generator.classes
# Get the label to class mapping from the generator
label2index = validation_generator.class_indices
# Getting the mapping from class index to class label
idx2label = dict((v,k) for k,v in label2index.items())
# Get the predictions from the model using the generator
predictions = model.predict_generator(validation_generator, steps=validation_generator.samples/validation_generator.batch_size,verbose=1)
predicted_classes = np.argmax(predictions,axis=1)
errors = np.where(predicted_classes != ground_truth)[0]
print("No of errors = {}/{}".format(len(errors),validation_generator.samples))
# Show the errors
for i in range(len(errors)):
pred_class = np.argmax(predictions[errors[i]])
pred_label = idx2label[pred_class]
title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(
fnames[errors[i]].split('/')[0],
pred_label,
predictions[errors[i]][pred_class])
original = load_img('{}/{}'.format(validation_dir,fnames[errors[i]]))
plt.figure(figsize=[7,7])
plt.axis('off')
plt.title(title)
plt.imshow(original)
plt.show()
The model code
from keras.engine import Model
vgg16_model = keras.applications.vgg16.VGG16()
input_layer2 = vgg16_model.input
vgg16_model.get_layer(index = 0).name = 'input2'
last_layer2 = vgg16_model.get_layer('block4_pool').output
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv1', padding='same')(last_layer2)
x = BatchNormalization(name='bn1')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv2', padding='same')(x)
x = BatchNormalization(name='bn2')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv3', padding='same')(x)
x = BatchNormalization(name='bn3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name = 'block5_pool')(x)
x = Flatten(name = 'flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
out2 = Dropout(0.3, name='dropout1')(x)
#Classification layer
output_layer2 = Dense(class_no, activation='softmax', name='prediction')(out2)
vgg16_face_model = Model(input_layer2, output_layer2)
vgg16_face_model.trainable = True
set_trainable = False
for layer in vgg16_face_model.layers:
if layer.name == 'block5_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
Please Help me..
python tensorflow machine-learning keras neural-network
asked Nov 21 '18 at 21:59
NoranNoran
1999
Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52
add a comment |
1
1
1
I am designing a deep learning model to classify images and I am using the following code to check the predication performance. It compares the images' labels with the predicted classes and then returns the error predication.
I was using sequential model, and this code was working fine. But, now it does not work fine.. predict_generator changes the order of the images. (predict_classes and validation_generator are not in the same order)
I want to make the prediction in the same order of the images in validation_generator, Do you know how can I do that??
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(image_size, image_size),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=False)
# Get the filenames from the generator
fnames = validation_generator.filenames
# Get the ground truth from generator
ground_truth = validation_generator.classes
# Get the label to class mapping from the generator
label2index = validation_generator.class_indices
# Getting the mapping from class index to class label
idx2label = dict((v,k) for k,v in label2index.items())
# Get the predictions from the model using the generator
predictions = model.predict_generator(validation_generator, steps=validation_generator.samples/validation_generator.batch_size,verbose=1)
predicted_classes = np.argmax(predictions,axis=1)
errors = np.where(predicted_classes != ground_truth)[0]
print("No of errors = {}/{}".format(len(errors),validation_generator.samples))
# Show the errors
for i in range(len(errors)):
pred_class = np.argmax(predictions[errors[i]])
pred_label = idx2label[pred_class]
title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(
fnames[errors[i]].split('/')[0],
pred_label,
predictions[errors[i]][pred_class])
original = load_img('{}/{}'.format(validation_dir,fnames[errors[i]]))
plt.figure(figsize=[7,7])
plt.axis('off')
plt.title(title)
plt.imshow(original)
plt.show()
The model code
from keras.engine import Model
vgg16_model = keras.applications.vgg16.VGG16()
input_layer2 = vgg16_model.input
vgg16_model.get_layer(index = 0).name = 'input2'
last_layer2 = vgg16_model.get_layer('block4_pool').output
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv1', padding='same')(last_layer2)
x = BatchNormalization(name='bn1')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv2', padding='same')(x)
x = BatchNormalization(name='bn2')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv3', padding='same')(x)
x = BatchNormalization(name='bn3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name = 'block5_pool')(x)
x = Flatten(name = 'flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
out2 = Dropout(0.3, name='dropout1')(x)
#Classification layer
output_layer2 = Dense(class_no, activation='softmax', name='prediction')(out2)
vgg16_face_model = Model(input_layer2, output_layer2)
vgg16_face_model.trainable = True
set_trainable = False
for layer in vgg16_face_model.layers:
if layer.name == 'block5_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
Please Help me..
python tensorflow machine-learning keras neural-network
asked Nov 21 '18 at 21:59
NoranNoran
1999
I am designing a deep learning model to classify images and I am using the following code to check the predication performance. It compares the images' labels with the predicted classes and then returns the error predication.
I was using sequential model, and this code was working fine. But, now it does not work fine.. predict_generator changes the order of the images. (predict_classes and validation_generator are not in the same order)
I want to make the prediction in the same order of the images in validation_generator, Do you know how can I do that??
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(image_size, image_size),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=False)
# Get the filenames from the generator
fnames = validation_generator.filenames
# Get the ground truth from generator
ground_truth = validation_generator.classes
# Get the label to class mapping from the generator
label2index = validation_generator.class_indices
# Getting the mapping from class index to class label
idx2label = dict((v,k) for k,v in label2index.items())
# Get the predictions from the model using the generator
predictions = model.predict_generator(validation_generator, steps=validation_generator.samples/validation_generator.batch_size,verbose=1)
predicted_classes = np.argmax(predictions,axis=1)
errors = np.where(predicted_classes != ground_truth)[0]
print("No of errors = {}/{}".format(len(errors),validation_generator.samples))
# Show the errors
for i in range(len(errors)):
pred_class = np.argmax(predictions[errors[i]])
pred_label = idx2label[pred_class]
title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(
fnames[errors[i]].split('/')[0],
pred_label,
predictions[errors[i]][pred_class])
original = load_img('{}/{}'.format(validation_dir,fnames[errors[i]]))
plt.figure(figsize=[7,7])
plt.axis('off')
plt.title(title)
plt.imshow(original)
plt.show()
The model code
from keras.engine import Model
vgg16_model = keras.applications.vgg16.VGG16()
input_layer2 = vgg16_model.input
vgg16_model.get_layer(index = 0).name = 'input2'
last_layer2 = vgg16_model.get_layer('block4_pool').output
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv1', padding='same')(last_layer2)
x = BatchNormalization(name='bn1')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv2', padding='same')(x)
x = BatchNormalization(name='bn2')(x)
x = Conv2D(512, (3, 3), activation='relu', name='block5_conv3', padding='same')(x)
x = BatchNormalization(name='bn3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name = 'block5_pool')(x)
x = Flatten(name = 'flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
out2 = Dropout(0.3, name='dropout1')(x)
#Classification layer
output_layer2 = Dense(class_no, activation='softmax', name='prediction')(out2)
vgg16_face_model = Model(input_layer2, output_layer2)
vgg16_face_model.trainable = True
set_trainable = False
for layer in vgg16_face_model.layers:
if layer.name == 'block5_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
Please Help me..
python tensorflow machine-learning keras neural-network
python tensorflow machine-learning keras neural-network
asked Nov 21 '18 at 21:59
NoranNoran
1999
asked Nov 21 '18 at 21:59
NoranNoran
1999
edited Nov 23 '18 at 21:20
Noran
asked Nov 21 '18 at 21:59
NoranNoran
1999
asked Nov 21 '18 at 21:59
NoranNoran
1999
asked Nov 21 '18 at 21:59
NoranNoran
1999
1999
Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52
add a comment |
Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52
Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52
add a comment |
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Where is the model?
– Milo Lu
Nov 22 '18 at 1:28
I have added the model .. please check it ..
– Noran
Nov 22 '18 at 6:52