adding metadata to tensorflow tflearn CNN
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I built a simple CNN network for (medical) image classification successfully, using tflearn. When I tried to add metadata to the CNN, I ran into this problem:ValueError: Cannot feed value of shape (96, 2) for Tensor 'TargetsData/Y:0', which has shape '(1390, 2)'. Any help is appreciated:
#extract pictures (0 thru 4095), next two bytes for the selection, and the rest for metadata
X, Y, Z = train_data[:,0:4096],train_data[:,4096:4098], train_data[:,4098:]
X = X.reshape([-1,64,64,1])
network = input_data(shape=[None, 64, 64, 1])
mdnetwork = input_data(shape=[None, 100])
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 30, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 100, activation='relu')
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
network = dropout(network, 0.5)
network = fully_connected(network, 50, activation='relu')
network = fully_connected(network, 2, activation='softmax')
# Train using classifier
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit([np.array(X).reshape(-1, 64, 64, 1), np.array(Z).reshape(-1, 100)], Y, n_epoch=5, shuffle=True, validation_set=0,
show_metric=True, batch_size=96, run_id='my_cnn')
model.save('my_cnn.tflearn')
tensorflow metadata conv-neural-network
asked Nov 12 at 5:54
nkumar
11
add a comment |
up vote
0
down vote
favorite
I built a simple CNN network for (medical) image classification successfully, using tflearn. When I tried to add metadata to the CNN, I ran into this problem:ValueError: Cannot feed value of shape (96, 2) for Tensor 'TargetsData/Y:0', which has shape '(1390, 2)'. Any help is appreciated:
#extract pictures (0 thru 4095), next two bytes for the selection, and the rest for metadata
X, Y, Z = train_data[:,0:4096],train_data[:,4096:4098], train_data[:,4098:]
X = X.reshape([-1,64,64,1])
network = input_data(shape=[None, 64, 64, 1])
mdnetwork = input_data(shape=[None, 100])
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 30, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 100, activation='relu')
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
network = dropout(network, 0.5)
network = fully_connected(network, 50, activation='relu')
network = fully_connected(network, 2, activation='softmax')
# Train using classifier
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit([np.array(X).reshape(-1, 64, 64, 1), np.array(Z).reshape(-1, 100)], Y, n_epoch=5, shuffle=True, validation_set=0,
show_metric=True, batch_size=96, run_id='my_cnn')
model.save('my_cnn.tflearn')
tensorflow metadata conv-neural-network
asked Nov 12 at 5:54
nkumar
11
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I built a simple CNN network for (medical) image classification successfully, using tflearn. When I tried to add metadata to the CNN, I ran into this problem:ValueError: Cannot feed value of shape (96, 2) for Tensor 'TargetsData/Y:0', which has shape '(1390, 2)'. Any help is appreciated:
#extract pictures (0 thru 4095), next two bytes for the selection, and the rest for metadata
X, Y, Z = train_data[:,0:4096],train_data[:,4096:4098], train_data[:,4098:]
X = X.reshape([-1,64,64,1])
network = input_data(shape=[None, 64, 64, 1])
mdnetwork = input_data(shape=[None, 100])
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 30, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 100, activation='relu')
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
network = dropout(network, 0.5)
network = fully_connected(network, 50, activation='relu')
network = fully_connected(network, 2, activation='softmax')
# Train using classifier
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit([np.array(X).reshape(-1, 64, 64, 1), np.array(Z).reshape(-1, 100)], Y, n_epoch=5, shuffle=True, validation_set=0,
show_metric=True, batch_size=96, run_id='my_cnn')
model.save('my_cnn.tflearn')
tensorflow metadata conv-neural-network
asked Nov 12 at 5:54
nkumar
11
I built a simple CNN network for (medical) image classification successfully, using tflearn. When I tried to add metadata to the CNN, I ran into this problem:ValueError: Cannot feed value of shape (96, 2) for Tensor 'TargetsData/Y:0', which has shape '(1390, 2)'. Any help is appreciated:
#extract pictures (0 thru 4095), next two bytes for the selection, and the rest for metadata
X, Y, Z = train_data[:,0:4096],train_data[:,4096:4098], train_data[:,4098:]
X = X.reshape([-1,64,64,1])
network = input_data(shape=[None, 64, 64, 1])
mdnetwork = input_data(shape=[None, 100])
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 30, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 40, 3, activation='relu')
network = conv_2d(network, 30, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 100, activation='relu')
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
network = dropout(network, 0.5)
network = fully_connected(network, 50, activation='relu')
network = fully_connected(network, 2, activation='softmax')
# Train using classifier
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit([np.array(X).reshape(-1, 64, 64, 1), np.array(Z).reshape(-1, 100)], Y, n_epoch=5, shuffle=True, validation_set=0,
show_metric=True, batch_size=96, run_id='my_cnn')
model.save('my_cnn.tflearn')
tensorflow metadata conv-neural-network
tensorflow metadata conv-neural-network
asked Nov 12 at 5:54
nkumar
11
asked Nov 12 at 5:54
nkumar
11
asked Nov 12 at 5:54
nkumar
11
asked Nov 12 at 5:54
nkumar
11
asked Nov 12 at 5:54
nkumar
11
11
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
It is good I can answer my question here! Anyway, I found the issue in my code above. It was a simple error. The error message led me astray. Here is the solution: Replace this code snippet
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
with
network = merge([network, mdnetwork], 'concat')
Thanks for those who read my request. Let me know if there are other options.
answered Nov 17 at 6:52
nkumar
11
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
It is good I can answer my question here! Anyway, I found the issue in my code above. It was a simple error. The error message led me astray. Here is the solution: Replace this code snippet
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
with
network = merge([network, mdnetwork], 'concat')
Thanks for those who read my request. Let me know if there are other options.
answered Nov 17 at 6:52
nkumar
11
add a comment |
up vote
0
down vote
It is good I can answer my question here! Anyway, I found the issue in my code above. It was a simple error. The error message led me astray. Here is the solution: Replace this code snippet
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
with
network = merge([network, mdnetwork], 'concat')
Thanks for those who read my request. Let me know if there are other options.
answered Nov 17 at 6:52
nkumar
11
add a comment |
up vote
0
down vote
up vote
0
down vote
It is good I can answer my question here! Anyway, I found the issue in my code above. It was a simple error. The error message led me astray. Here is the solution: Replace this code snippet
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
with
network = merge([network, mdnetwork], 'concat')
Thanks for those who read my request. Let me know if there are other options.
answered Nov 17 at 6:52
nkumar
11
It is good I can answer my question here! Anyway, I found the issue in my code above. It was a simple error. The error message led me astray. Here is the solution: Replace this code snippet
Zt= fully_connected(Z, 100, activation='relu')
network = merge([network,Zt], 'concat')
with
network = merge([network, mdnetwork], 'concat')
Thanks for those who read my request. Let me know if there are other options.
answered Nov 17 at 6:52
nkumar
11
answered Nov 17 at 6:52
nkumar
11
answered Nov 17 at 6:52
nkumar
11
answered Nov 17 at 6:52
nkumar
11
11
add a comment |
add a comment |
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