Meaning of weights from a 1D convolutional network
$begingroup$
just trying to better understand what's happening in my network. I built a 3 layer convolutional network to classify 1D signals. Here it is:
model = Sequential()
model.add(Conv1D(32, 12, activation='relu', input_shape=(1500, 1)))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(64, 12, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(128, 12, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
I have 224 (32 + 64 + 128) filters each of size 12. I expect each of my signals (size 1500) to pass through a filter, have the activation function applied, pooled, and repeat...
So, when all is said and done, I expect 224 filters each with 12 weights (32x12), (64x12), and (128x12). But, when I use get_weights() I get a completely different shape. I opened the cell up in Matlab, and it looks like this:
Can anyone help me understand what I'm missing? Why does each pooling layer have a number of weights equal to the number of filters from the convolutional layer before? (cell 2 is a 1x32 vector)(is that what's happening?)
I'm trying to visualize what is happening to a random signal that passes through these layers, but haven't found much in the way for visualization for 1D signals. I'd like to extract the filters and and save them, then convolve manually.
I feel like I've completely misunderstood how these things work.
classification time-series cnn visualization
asked 7 mins ago
B. EricksonB. Erickson
1
$endgroup$
add a comment |
$begingroup$
just trying to better understand what's happening in my network. I built a 3 layer convolutional network to classify 1D signals. Here it is:
model = Sequential()
model.add(Conv1D(32, 12, activation='relu', input_shape=(1500, 1)))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(64, 12, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(128, 12, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
I have 224 (32 + 64 + 128) filters each of size 12. I expect each of my signals (size 1500) to pass through a filter, have the activation function applied, pooled, and repeat...
So, when all is said and done, I expect 224 filters each with 12 weights (32x12), (64x12), and (128x12). But, when I use get_weights() I get a completely different shape. I opened the cell up in Matlab, and it looks like this:
Can anyone help me understand what I'm missing? Why does each pooling layer have a number of weights equal to the number of filters from the convolutional layer before? (cell 2 is a 1x32 vector)(is that what's happening?)
I'm trying to visualize what is happening to a random signal that passes through these layers, but haven't found much in the way for visualization for 1D signals. I'd like to extract the filters and and save them, then convolve manually.
I feel like I've completely misunderstood how these things work.
classification time-series cnn visualization
asked 7 mins ago
B. EricksonB. Erickson
1
$endgroup$
$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago
add a comment |
$begingroup$
just trying to better understand what's happening in my network. I built a 3 layer convolutional network to classify 1D signals. Here it is:
model = Sequential()
model.add(Conv1D(32, 12, activation='relu', input_shape=(1500, 1)))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(64, 12, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(128, 12, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
I have 224 (32 + 64 + 128) filters each of size 12. I expect each of my signals (size 1500) to pass through a filter, have the activation function applied, pooled, and repeat...
So, when all is said and done, I expect 224 filters each with 12 weights (32x12), (64x12), and (128x12). But, when I use get_weights() I get a completely different shape. I opened the cell up in Matlab, and it looks like this:
Can anyone help me understand what I'm missing? Why does each pooling layer have a number of weights equal to the number of filters from the convolutional layer before? (cell 2 is a 1x32 vector)(is that what's happening?)
I'm trying to visualize what is happening to a random signal that passes through these layers, but haven't found much in the way for visualization for 1D signals. I'd like to extract the filters and and save them, then convolve manually.
I feel like I've completely misunderstood how these things work.
classification time-series cnn visualization
asked 7 mins ago
B. EricksonB. Erickson
1
$endgroup$
just trying to better understand what's happening in my network. I built a 3 layer convolutional network to classify 1D signals. Here it is:
model = Sequential()
model.add(Conv1D(32, 12, activation='relu', input_shape=(1500, 1)))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(64, 12, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Dropout(0.5))
model.add(Conv1D(128, 12, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
I have 224 (32 + 64 + 128) filters each of size 12. I expect each of my signals (size 1500) to pass through a filter, have the activation function applied, pooled, and repeat...
So, when all is said and done, I expect 224 filters each with 12 weights (32x12), (64x12), and (128x12). But, when I use get_weights() I get a completely different shape. I opened the cell up in Matlab, and it looks like this:
Can anyone help me understand what I'm missing? Why does each pooling layer have a number of weights equal to the number of filters from the convolutional layer before? (cell 2 is a 1x32 vector)(is that what's happening?)
I'm trying to visualize what is happening to a random signal that passes through these layers, but haven't found much in the way for visualization for 1D signals. I'd like to extract the filters and and save them, then convolve manually.
I feel like I've completely misunderstood how these things work.
classification time-series cnn visualization
classification time-series cnn visualization
asked 7 mins ago
B. EricksonB. Erickson
1
asked 7 mins ago
B. EricksonB. Erickson
1
asked 7 mins ago
B. EricksonB. Erickson
1
asked 7 mins ago
B. EricksonB. Erickson
1
asked 7 mins ago
B. EricksonB. Erickson
1
1
$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago
add a comment |
$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago
$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago
$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago
add a comment |
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$begingroup$
AHH! The pooling layer weights are not from the pooling layer, because that's dumb. They're the weights on the synapses between the layers of neurons, correct?
$endgroup$
– B. Erickson
4 mins ago