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FailedPreconditionError: Attempting to use uninitialized value Adam/lr
Exception in Tensorflow function used as Keras custom lossGetting very low categorical_accuracy while refitting loaded Keras modelKeras AttributeError: 'list' object has no attribute 'ndim'Precision@n and Recall@n in Keras Neural NetworkKeras: Accuracy Drops While Finetuning Inceptionscipy.ndimage.zoom is taking long time even on small arraysError when loading Keras model trained by tensorflowInput 0 is incompatible with layer flatten_5: expected min_ndim=3, found ndim=2Is it possible to train a CNN starting at an intermediate layer (in general and in Keras)?'Sequential' object has no attribute 'loss' - When I used GridSearchCV to tuning my Keras model
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
Hello I am new to machine learning. I was on the process of training a VGG16 fine-tuned model.After the first epoch,the program stopped and gave this error:

Below is the code I used for the model:
# create a copy of a mobilenet model
import keras
vgg_model=keras.applications.vgg16.VGG16()
type(vgg_model)
vgg_model.summary()
from keras.models import Sequential
model = Sequential()
for layer in vgg_model.layers[:-1]:
model.add(layer)
model.summary()
# CREATE THE MODEL ARCHITECTURE
from keras.layers import Dense, Activation, Dropout
model.add(Dropout(0.25))
model.add(Dense(7,activation='softmax'))
model.summary()
#Train the Model
# Define Top2 and Top3 Accuracy
from keras.metrics import categorical_accuracy, top_k_categorical_accuracy
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
def top_2_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=2)
from keras.optimizers import Adam
model.compile(Adam(lr=0.01), loss='categorical_crossentropy',
metrics=[categorical_accuracy, top_2_accuracy, top_3_accuracy])
# Get the labels that are associated with each index
print(valid_batches.class_indices)
# Add weights to try to make the model more sensitive to melanoma
class_weights=
0: 1.0, # akiec
1: 1.0, # bcc
2: 1.0, # bkl
3: 1.0, # df
4: 3.0, # mel # Try to make the model more sensitive to Melanoma.
5: 1.0, # nv
6: 1.0, # vasc
filepath = "skin.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1,
save_best_only=True, mode='max')
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2,
verbose=1, mode='max', min_lr=0.00001)
callbacks_list = [checkpoint, reduce_lr]
history = model.fit_generator(train_batches, steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=valid_batches,
validation_steps=val_steps,
epochs=40, verbose=1,
callbacks=callbacks_list)
I am trying to learn how to fine tune, train and use VGG16 model on image dataset. I was following this blog where he used mobileNet.
I was following this VGG16 tutorial to write the code for the model.
If anyone can help me fix this error or explain how and why it happened I would highly appreciate your help.
Thank you so much.
Dependencies:
- tensorflow 1.12.0
- tensorflow-gpu 1.12.0
- python 3.6.0
- keras 2.2.4
tensorflow keras training-data vgg-net finetunning
add a comment |
Hello I am new to machine learning. I was on the process of training a VGG16 fine-tuned model.After the first epoch,the program stopped and gave this error:

Below is the code I used for the model:
# create a copy of a mobilenet model
import keras
vgg_model=keras.applications.vgg16.VGG16()
type(vgg_model)
vgg_model.summary()
from keras.models import Sequential
model = Sequential()
for layer in vgg_model.layers[:-1]:
model.add(layer)
model.summary()
# CREATE THE MODEL ARCHITECTURE
from keras.layers import Dense, Activation, Dropout
model.add(Dropout(0.25))
model.add(Dense(7,activation='softmax'))
model.summary()
#Train the Model
# Define Top2 and Top3 Accuracy
from keras.metrics import categorical_accuracy, top_k_categorical_accuracy
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
def top_2_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=2)
from keras.optimizers import Adam
model.compile(Adam(lr=0.01), loss='categorical_crossentropy',
metrics=[categorical_accuracy, top_2_accuracy, top_3_accuracy])
# Get the labels that are associated with each index
print(valid_batches.class_indices)
# Add weights to try to make the model more sensitive to melanoma
class_weights=
0: 1.0, # akiec
1: 1.0, # bcc
2: 1.0, # bkl
3: 1.0, # df
4: 3.0, # mel # Try to make the model more sensitive to Melanoma.
5: 1.0, # nv
6: 1.0, # vasc
filepath = "skin.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1,
save_best_only=True, mode='max')
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2,
verbose=1, mode='max', min_lr=0.00001)
callbacks_list = [checkpoint, reduce_lr]
history = model.fit_generator(train_batches, steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=valid_batches,
validation_steps=val_steps,
epochs=40, verbose=1,
callbacks=callbacks_list)
I am trying to learn how to fine tune, train and use VGG16 model on image dataset. I was following this blog where he used mobileNet.
I was following this VGG16 tutorial to write the code for the model.
If anyone can help me fix this error or explain how and why it happened I would highly appreciate your help.
Thank you so much.
Dependencies:
- tensorflow 1.12.0
- tensorflow-gpu 1.12.0
- python 3.6.0
- keras 2.2.4
tensorflow keras training-data vgg-net finetunning
Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14
add a comment |
Hello I am new to machine learning. I was on the process of training a VGG16 fine-tuned model.After the first epoch,the program stopped and gave this error:

Below is the code I used for the model:
# create a copy of a mobilenet model
import keras
vgg_model=keras.applications.vgg16.VGG16()
type(vgg_model)
vgg_model.summary()
from keras.models import Sequential
model = Sequential()
for layer in vgg_model.layers[:-1]:
model.add(layer)
model.summary()
# CREATE THE MODEL ARCHITECTURE
from keras.layers import Dense, Activation, Dropout
model.add(Dropout(0.25))
model.add(Dense(7,activation='softmax'))
model.summary()
#Train the Model
# Define Top2 and Top3 Accuracy
from keras.metrics import categorical_accuracy, top_k_categorical_accuracy
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
def top_2_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=2)
from keras.optimizers import Adam
model.compile(Adam(lr=0.01), loss='categorical_crossentropy',
metrics=[categorical_accuracy, top_2_accuracy, top_3_accuracy])
# Get the labels that are associated with each index
print(valid_batches.class_indices)
# Add weights to try to make the model more sensitive to melanoma
class_weights=
0: 1.0, # akiec
1: 1.0, # bcc
2: 1.0, # bkl
3: 1.0, # df
4: 3.0, # mel # Try to make the model more sensitive to Melanoma.
5: 1.0, # nv
6: 1.0, # vasc
filepath = "skin.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1,
save_best_only=True, mode='max')
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2,
verbose=1, mode='max', min_lr=0.00001)
callbacks_list = [checkpoint, reduce_lr]
history = model.fit_generator(train_batches, steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=valid_batches,
validation_steps=val_steps,
epochs=40, verbose=1,
callbacks=callbacks_list)
I am trying to learn how to fine tune, train and use VGG16 model on image dataset. I was following this blog where he used mobileNet.
I was following this VGG16 tutorial to write the code for the model.
If anyone can help me fix this error or explain how and why it happened I would highly appreciate your help.
Thank you so much.
Dependencies:
- tensorflow 1.12.0
- tensorflow-gpu 1.12.0
- python 3.6.0
- keras 2.2.4
tensorflow keras training-data vgg-net finetunning
Hello I am new to machine learning. I was on the process of training a VGG16 fine-tuned model.After the first epoch,the program stopped and gave this error:

Below is the code I used for the model:
# create a copy of a mobilenet model
import keras
vgg_model=keras.applications.vgg16.VGG16()
type(vgg_model)
vgg_model.summary()
from keras.models import Sequential
model = Sequential()
for layer in vgg_model.layers[:-1]:
model.add(layer)
model.summary()
# CREATE THE MODEL ARCHITECTURE
from keras.layers import Dense, Activation, Dropout
model.add(Dropout(0.25))
model.add(Dense(7,activation='softmax'))
model.summary()
#Train the Model
# Define Top2 and Top3 Accuracy
from keras.metrics import categorical_accuracy, top_k_categorical_accuracy
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
def top_2_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=2)
from keras.optimizers import Adam
model.compile(Adam(lr=0.01), loss='categorical_crossentropy',
metrics=[categorical_accuracy, top_2_accuracy, top_3_accuracy])
# Get the labels that are associated with each index
print(valid_batches.class_indices)
# Add weights to try to make the model more sensitive to melanoma
class_weights=
0: 1.0, # akiec
1: 1.0, # bcc
2: 1.0, # bkl
3: 1.0, # df
4: 3.0, # mel # Try to make the model more sensitive to Melanoma.
5: 1.0, # nv
6: 1.0, # vasc
filepath = "skin.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1,
save_best_only=True, mode='max')
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2,
verbose=1, mode='max', min_lr=0.00001)
callbacks_list = [checkpoint, reduce_lr]
history = model.fit_generator(train_batches, steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=valid_batches,
validation_steps=val_steps,
epochs=40, verbose=1,
callbacks=callbacks_list)
I am trying to learn how to fine tune, train and use VGG16 model on image dataset. I was following this blog where he used mobileNet.
I was following this VGG16 tutorial to write the code for the model.
If anyone can help me fix this error or explain how and why it happened I would highly appreciate your help.
Thank you so much.
Dependencies:
- tensorflow 1.12.0
- tensorflow-gpu 1.12.0
- python 3.6.0
- keras 2.2.4
tensorflow keras training-data vgg-net finetunning
tensorflow keras training-data vgg-net finetunning
asked Mar 25 at 19:35
RstynblRstynbl
201 silver badge5 bronze badges
201 silver badge5 bronze badges
Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14
add a comment |
Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14
Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14
Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14
add a comment |
1 Answer
1
active
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I had the same error when I used the ReduceLROnPlateau callback. Unless it's absolutely necessary, you can maybe omit its usage.
add a comment |
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I had the same error when I used the ReduceLROnPlateau callback. Unless it's absolutely necessary, you can maybe omit its usage.
add a comment |
I had the same error when I used the ReduceLROnPlateau callback. Unless it's absolutely necessary, you can maybe omit its usage.
add a comment |
I had the same error when I used the ReduceLROnPlateau callback. Unless it's absolutely necessary, you can maybe omit its usage.
I had the same error when I used the ReduceLROnPlateau callback. Unless it's absolutely necessary, you can maybe omit its usage.
answered Apr 1 at 7:54
kielninokielnino
333 bronze badges
333 bronze badges
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Are you trying to load pretrained weights?
– Sharky
Mar 25 at 20:14