ValueError: Tensor Tensor(“activation_5/Softmax:0”, shape=(?, 2), dtype=float32) is not an element of this graph The 2019 Stack Overflow Developer Survey Results Are InTensorflow regression predicting 1 for all inputsKeras LSTM: use weights from Keras model to replicate predictions using numpyVisualizing ConvNet filters using my own fine-tuned network resulting in a “NoneType” when running: K.gradients(loss, model.input)[0]Simple prediction with KerasValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)How to set input for proper fit with lstm?Training Accuracy stuck in KerasValue error in Merging two different models in kerasCannot interpret feed_dict key as Tensor: Tensor Tensor(“Placeholder:0”, shape=(3, 3, 3, 32), dtype=float32) is not an element of this graphWhat is the meaning of ValueError in Keras? - 'Tensor conversion requested dtype complex64 for Tensor with dtype float32'
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ValueError: Tensor Tensor(“activation_5/Softmax:0”, shape=(?, 2), dtype=float32) is not an element of this graph
The 2019 Stack Overflow Developer Survey Results Are InTensorflow regression predicting 1 for all inputsKeras LSTM: use weights from Keras model to replicate predictions using numpyVisualizing ConvNet filters using my own fine-tuned network resulting in a “NoneType” when running: K.gradients(loss, model.input)[0]Simple prediction with KerasValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)How to set input for proper fit with lstm?Training Accuracy stuck in KerasValue error in Merging two different models in kerasCannot interpret feed_dict key as Tensor: Tensor Tensor(“Placeholder:0”, shape=(3, 3, 3, 32), dtype=float32) is not an element of this graphWhat is the meaning of ValueError in Keras? - 'Tensor conversion requested dtype complex64 for Tensor with dtype float32'
$begingroup$
There seem to be an issue with predicting using my keras model. I had trained it using the following keras code:
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(2))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
However when i predict it on my local system after training with the shape (1,150,150,3) . It predicts accurately with an accuracy over 90%. however when i load my model on my raspberry pi and input the image of the same shape (1,150,150,3) it returns an error. Below is the code loaded on the raspberry pi to predict from the keras model.
data = numpy.fromstring(stream.getvalue() , dtype = numpy.uint8)
image5 = cv.imdecode(data , 1)
print(image5.shape)
#cv.imwrite('uhhu.png',image5)
img = cv.resize(image5,(150,150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
x = x/255
x = numpy.float32(x)
print(x.shape)
score = loaded_model.predict(x)
print(score)
neural-network deep-learning keras
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
There seem to be an issue with predicting using my keras model. I had trained it using the following keras code:
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(2))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
However when i predict it on my local system after training with the shape (1,150,150,3) . It predicts accurately with an accuracy over 90%. however when i load my model on my raspberry pi and input the image of the same shape (1,150,150,3) it returns an error. Below is the code loaded on the raspberry pi to predict from the keras model.
data = numpy.fromstring(stream.getvalue() , dtype = numpy.uint8)
image5 = cv.imdecode(data , 1)
print(image5.shape)
#cv.imwrite('uhhu.png',image5)
img = cv.resize(image5,(150,150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
x = x/255
x = numpy.float32(x)
print(x.shape)
score = loaded_model.predict(x)
print(score)
neural-network deep-learning keras
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
There seem to be an issue with predicting using my keras model. I had trained it using the following keras code:
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(2))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
However when i predict it on my local system after training with the shape (1,150,150,3) . It predicts accurately with an accuracy over 90%. however when i load my model on my raspberry pi and input the image of the same shape (1,150,150,3) it returns an error. Below is the code loaded on the raspberry pi to predict from the keras model.
data = numpy.fromstring(stream.getvalue() , dtype = numpy.uint8)
image5 = cv.imdecode(data , 1)
print(image5.shape)
#cv.imwrite('uhhu.png',image5)
img = cv.resize(image5,(150,150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
x = x/255
x = numpy.float32(x)
print(x.shape)
score = loaded_model.predict(x)
print(score)
neural-network deep-learning keras
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
There seem to be an issue with predicting using my keras model. I had trained it using the following keras code:
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(2))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
However when i predict it on my local system after training with the shape (1,150,150,3) . It predicts accurately with an accuracy over 90%. however when i load my model on my raspberry pi and input the image of the same shape (1,150,150,3) it returns an error. Below is the code loaded on the raspberry pi to predict from the keras model.
data = numpy.fromstring(stream.getvalue() , dtype = numpy.uint8)
image5 = cv.imdecode(data , 1)
print(image5.shape)
#cv.imwrite('uhhu.png',image5)
img = cv.resize(image5,(150,150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
x = x/255
x = numpy.float32(x)
print(x.shape)
score = loaded_model.predict(x)
print(score)
neural-network deep-learning keras
neural-network deep-learning keras
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
asked 10 hours ago
Zahid AhmedZahid Ahmed
64
64
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Zahid Ahmed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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