Keras Classifier returns similar output for all PredictionsMultiple output classes in kerasKeras stateful LSTM returns NaN for validation lossGet multiple output from KerasMultilabel multiclass classifier returns same probabilities for any inputKeras single sample prediction returns different valuesValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)How to use Keras Linear Regression for Multiple input-output?Is regularization included in loss history Keras returns?predict gives the same output value for every image (Keras)
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Keras Classifier returns similar output for all Predictions
Multiple output classes in kerasKeras stateful LSTM returns NaN for validation lossGet multiple output from KerasMultilabel multiclass classifier returns same probabilities for any inputKeras single sample prediction returns different valuesValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)How to use Keras Linear Regression for Multiple input-output?Is regularization included in loss history Keras returns?predict gives the same output value for every image (Keras)
$begingroup$
I completed training the model with an accuracy of 1.000 and a validation accuracy of 0.9565. Unfortunately whenever i input a image into my model i get the same output regardless. Am i doing something wrong when predicting or during my training. W and A are my class labels.
My folder structure for the image generators are as follows:
images/
a/
a001.jpg.png..
w/
w002.jpg.png..
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2)
test_datagen = ImageDataGenerator(rescale=1./255)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
batch_size = 64
# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\train', # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=batch_size,
color_mode='rgb',
class_mode='binary') # since we use binary_crossentropy loss, we need binary labels
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\val',
target_size=(150, 150),
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50,
validation_data=validation_generator,
validation_steps=800 // batch_size)
model.save_weights('first_try.h5')
img = cv2.imread("C:\Users\Zahid\Desktop\Data\TrainingData\images\a\img_0201.jpg.png")
resized_image = cv2.resize(image, (150, 150))
x = img_to_array(resized_image)
x = x.reshape((1,) + x.shape)
x = x/255
print(x.shape)
scores_train = model.predict(x)
print(scores_train)
neural-network keras dataset
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
$endgroup$
add a comment |
$begingroup$
I completed training the model with an accuracy of 1.000 and a validation accuracy of 0.9565. Unfortunately whenever i input a image into my model i get the same output regardless. Am i doing something wrong when predicting or during my training. W and A are my class labels.
My folder structure for the image generators are as follows:
images/
a/
a001.jpg.png..
w/
w002.jpg.png..
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2)
test_datagen = ImageDataGenerator(rescale=1./255)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
batch_size = 64
# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\train', # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=batch_size,
color_mode='rgb',
class_mode='binary') # since we use binary_crossentropy loss, we need binary labels
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\val',
target_size=(150, 150),
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50,
validation_data=validation_generator,
validation_steps=800 // batch_size)
model.save_weights('first_try.h5')
img = cv2.imread("C:\Users\Zahid\Desktop\Data\TrainingData\images\a\img_0201.jpg.png")
resized_image = cv2.resize(image, (150, 150))
x = img_to_array(resized_image)
x = x.reshape((1,) + x.shape)
x = x/255
print(x.shape)
scores_train = model.predict(x)
print(scores_train)
neural-network keras dataset
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
$endgroup$
$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
1
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43
add a comment |
$begingroup$
I completed training the model with an accuracy of 1.000 and a validation accuracy of 0.9565. Unfortunately whenever i input a image into my model i get the same output regardless. Am i doing something wrong when predicting or during my training. W and A are my class labels.
My folder structure for the image generators are as follows:
images/
a/
a001.jpg.png..
w/
w002.jpg.png..
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2)
test_datagen = ImageDataGenerator(rescale=1./255)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
batch_size = 64
# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\train', # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=batch_size,
color_mode='rgb',
class_mode='binary') # since we use binary_crossentropy loss, we need binary labels
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\val',
target_size=(150, 150),
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50,
validation_data=validation_generator,
validation_steps=800 // batch_size)
model.save_weights('first_try.h5')
img = cv2.imread("C:\Users\Zahid\Desktop\Data\TrainingData\images\a\img_0201.jpg.png")
resized_image = cv2.resize(image, (150, 150))
x = img_to_array(resized_image)
x = x.reshape((1,) + x.shape)
x = x/255
print(x.shape)
scores_train = model.predict(x)
print(scores_train)
neural-network keras dataset
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
$endgroup$
I completed training the model with an accuracy of 1.000 and a validation accuracy of 0.9565. Unfortunately whenever i input a image into my model i get the same output regardless. Am i doing something wrong when predicting or during my training. W and A are my class labels.
My folder structure for the image generators are as follows:
images/
a/
a001.jpg.png..
w/
w002.jpg.png..
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2)
test_datagen = ImageDataGenerator(rescale=1./255)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
batch_size = 64
# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\train', # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=batch_size,
color_mode='rgb',
class_mode='binary') # since we use binary_crossentropy loss, we need binary labels
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'C:\Users\Zahid\Desktop\Dataset\val',
target_size=(150, 150),
batch_size=batch_size,
color_mode='rgb',
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50,
validation_data=validation_generator,
validation_steps=800 // batch_size)
model.save_weights('first_try.h5')
img = cv2.imread("C:\Users\Zahid\Desktop\Data\TrainingData\images\a\img_0201.jpg.png")
resized_image = cv2.resize(image, (150, 150))
x = img_to_array(resized_image)
x = x.reshape((1,) + x.shape)
x = x/255
print(x.shape)
scores_train = model.predict(x)
print(scores_train)
neural-network keras dataset
neural-network keras dataset
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
asked Apr 6 at 13:32
Zahid AhmedZahid Ahmed
64
64
$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
1
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43
add a comment |
$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
1
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43
$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
1
1
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
As you are using sigmoid as activation function in last layer. It will output generate output based on if probability above 50% then it belongs to "W" class and if it is less than 50% belongs to "A" class.
If you can print output probability of different images & share it then it will little helpful for us for understand problem
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
$endgroup$
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
add a comment |
$begingroup$
The issue was fixed by changing the Dense Layer to 2 hence specifying two classes, and also switching the Sigmoid activation function with a Softmax function.
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
$endgroup$
add a comment |
Your Answer
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
As you are using sigmoid as activation function in last layer. It will output generate output based on if probability above 50% then it belongs to "W" class and if it is less than 50% belongs to "A" class.
If you can print output probability of different images & share it then it will little helpful for us for understand problem
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
$endgroup$
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
add a comment |
$begingroup$
As you are using sigmoid as activation function in last layer. It will output generate output based on if probability above 50% then it belongs to "W" class and if it is less than 50% belongs to "A" class.
If you can print output probability of different images & share it then it will little helpful for us for understand problem
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
$endgroup$
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
add a comment |
$begingroup$
As you are using sigmoid as activation function in last layer. It will output generate output based on if probability above 50% then it belongs to "W" class and if it is less than 50% belongs to "A" class.
If you can print output probability of different images & share it then it will little helpful for us for understand problem
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
$endgroup$
As you are using sigmoid as activation function in last layer. It will output generate output based on if probability above 50% then it belongs to "W" class and if it is less than 50% belongs to "A" class.
If you can print output probability of different images & share it then it will little helpful for us for understand problem
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
answered Apr 6 at 15:32
Swapnil PoteSwapnil Pote
11
11
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
add a comment |
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
$begingroup$
The output probability remains the same regardless of the image at 3.2287784e-15.
$endgroup$
– Zahid Ahmed
Apr 6 at 16:28
add a comment |
$begingroup$
The issue was fixed by changing the Dense Layer to 2 hence specifying two classes, and also switching the Sigmoid activation function with a Softmax function.
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
$endgroup$
add a comment |
$begingroup$
The issue was fixed by changing the Dense Layer to 2 hence specifying two classes, and also switching the Sigmoid activation function with a Softmax function.
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
$endgroup$
add a comment |
$begingroup$
The issue was fixed by changing the Dense Layer to 2 hence specifying two classes, and also switching the Sigmoid activation function with a Softmax function.
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
$endgroup$
The issue was fixed by changing the Dense Layer to 2 hence specifying two classes, and also switching the Sigmoid activation function with a Softmax function.
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
answered Apr 7 at 18:01
Zahid AhmedZahid Ahmed
64
64
add a comment |
add a comment |
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$begingroup$
what is number 1 in your last dense layer? Do you have only one class? and why you use a sigmoid function in the output layer? try using softmax function, it is better.
$endgroup$
– SoK
Apr 6 at 13:40
1
$begingroup$
@honas.cs I Have two classes as mentioned in the question , and i followed a keras example to train this model. As shown in my folder structure i have seperated the classes into two seperate folders and trained them.
$endgroup$
– Zahid Ahmed
Apr 6 at 13:43