Adding dense layer to CNN causes to stop learning
$begingroup$
I have 6 layer CNN model that (kind of) works with "GlobalAveragePooling2D()" in the end but instead if I flatten and add dense layers like below, it just does not learn. After 20 epochs loss and accuracy curves still doesn't change at all.
Do you think anything wrong the way I flatten or add dense layers in the end? I also printed out the shapes incase it helps.
model.add(Ativation('relu'))
model.add(BatchNormalization())
print("Conv6 after shape",model.output_shape)
model.add(Convolution2D(filters=64, kernel_size=(4,4),
strides=(2,2),
padding="same",
kernel_regularizer=l2(0.0001),
kernel_initializer="normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
print("before flat shape",model.output_shape)
model.add(Flatten())
model.add(Dropout(0.5))
print("after flat shape",model.output_shape)
model.add(Dense(200, activation='relu'))
print("after dense shape",model.output_shape)
model.add(Dense(2, activation='softmax'))
And here is output of full model:
0 shape (None, 1, 44100, 40)
1 shape (None, 1, 44100, 40)
2 shape (None, 1, 160, 40)
Conv3 after shape (None, 40, 160, 24)
Conv4 after shape (None, 40, 160, 24)
Conv5 after shape (None, 20, 80, 48)
Conv6 after shape (None, 10, 40, 48)
before flat shape (None, 5, 20, 64)
after flat shape (None, 6400)
after dense shape (None, 200)
machine-learning neural-network keras tensorflow
asked 7 mins ago
SpringSpring
1255
$endgroup$
add a comment |
$begingroup$
I have 6 layer CNN model that (kind of) works with "GlobalAveragePooling2D()" in the end but instead if I flatten and add dense layers like below, it just does not learn. After 20 epochs loss and accuracy curves still doesn't change at all.
Do you think anything wrong the way I flatten or add dense layers in the end? I also printed out the shapes incase it helps.
model.add(Ativation('relu'))
model.add(BatchNormalization())
print("Conv6 after shape",model.output_shape)
model.add(Convolution2D(filters=64, kernel_size=(4,4),
strides=(2,2),
padding="same",
kernel_regularizer=l2(0.0001),
kernel_initializer="normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
print("before flat shape",model.output_shape)
model.add(Flatten())
model.add(Dropout(0.5))
print("after flat shape",model.output_shape)
model.add(Dense(200, activation='relu'))
print("after dense shape",model.output_shape)
model.add(Dense(2, activation='softmax'))
And here is output of full model:
0 shape (None, 1, 44100, 40)
1 shape (None, 1, 44100, 40)
2 shape (None, 1, 160, 40)
Conv3 after shape (None, 40, 160, 24)
Conv4 after shape (None, 40, 160, 24)
Conv5 after shape (None, 20, 80, 48)
Conv6 after shape (None, 10, 40, 48)
before flat shape (None, 5, 20, 64)
after flat shape (None, 6400)
after dense shape (None, 200)
machine-learning neural-network keras tensorflow
asked 7 mins ago
SpringSpring
1255
$endgroup$
add a comment |
$begingroup$
I have 6 layer CNN model that (kind of) works with "GlobalAveragePooling2D()" in the end but instead if I flatten and add dense layers like below, it just does not learn. After 20 epochs loss and accuracy curves still doesn't change at all.
Do you think anything wrong the way I flatten or add dense layers in the end? I also printed out the shapes incase it helps.
model.add(Ativation('relu'))
model.add(BatchNormalization())
print("Conv6 after shape",model.output_shape)
model.add(Convolution2D(filters=64, kernel_size=(4,4),
strides=(2,2),
padding="same",
kernel_regularizer=l2(0.0001),
kernel_initializer="normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
print("before flat shape",model.output_shape)
model.add(Flatten())
model.add(Dropout(0.5))
print("after flat shape",model.output_shape)
model.add(Dense(200, activation='relu'))
print("after dense shape",model.output_shape)
model.add(Dense(2, activation='softmax'))
And here is output of full model:
0 shape (None, 1, 44100, 40)
1 shape (None, 1, 44100, 40)
2 shape (None, 1, 160, 40)
Conv3 after shape (None, 40, 160, 24)
Conv4 after shape (None, 40, 160, 24)
Conv5 after shape (None, 20, 80, 48)
Conv6 after shape (None, 10, 40, 48)
before flat shape (None, 5, 20, 64)
after flat shape (None, 6400)
after dense shape (None, 200)
machine-learning neural-network keras tensorflow
asked 7 mins ago
SpringSpring
1255
$endgroup$
I have 6 layer CNN model that (kind of) works with "GlobalAveragePooling2D()" in the end but instead if I flatten and add dense layers like below, it just does not learn. After 20 epochs loss and accuracy curves still doesn't change at all.
Do you think anything wrong the way I flatten or add dense layers in the end? I also printed out the shapes incase it helps.
model.add(Ativation('relu'))
model.add(BatchNormalization())
print("Conv6 after shape",model.output_shape)
model.add(Convolution2D(filters=64, kernel_size=(4,4),
strides=(2,2),
padding="same",
kernel_regularizer=l2(0.0001),
kernel_initializer="normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
print("before flat shape",model.output_shape)
model.add(Flatten())
model.add(Dropout(0.5))
print("after flat shape",model.output_shape)
model.add(Dense(200, activation='relu'))
print("after dense shape",model.output_shape)
model.add(Dense(2, activation='softmax'))
And here is output of full model:
0 shape (None, 1, 44100, 40)
1 shape (None, 1, 44100, 40)
2 shape (None, 1, 160, 40)
Conv3 after shape (None, 40, 160, 24)
Conv4 after shape (None, 40, 160, 24)
Conv5 after shape (None, 20, 80, 48)
Conv6 after shape (None, 10, 40, 48)
before flat shape (None, 5, 20, 64)
after flat shape (None, 6400)
after dense shape (None, 200)
machine-learning neural-network keras tensorflow
machine-learning neural-network keras tensorflow
asked 7 mins ago
SpringSpring
1255
asked 7 mins ago
SpringSpring
1255
asked 7 mins ago
SpringSpring
1255
asked 7 mins ago
SpringSpring
1255
asked 7 mins ago
SpringSpring
1255
1255
add a comment |
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