keras use in Convolution1D
This article illustrates two things, one is Convolution1D introduction, the other is model.summary () is used.
First of all let me say at model.summary (), this method can print out the information model, readers can view the contents of each output.
Then say under Convolution1D uses, Convolution1D one-dimensional convolution, is mainly used to filter element adjacent one-dimensional input, official documents like this
keras.layers.convolutional.Convolution1D(nb_filter, filter_length, init='glorot_uniform', activation=None, weights=None, border_mode='valid', subsample_length=1, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None, input_length=None) Then the official gives examples of this is the
# apply a convolution 1d of length 3 to a sequence with 10 timesteps, # with 64 output filters model = Sequential() model.add(Convolution1D(64, 3, border_mode='same', input_shape=(10, 32))) # now model.output_shape == (None, 10, 64) # add a new conv1d on top model.add(Convolution1D(32, 3, border_mode='same')) # now model.output_shape == (None, 10, 32)
Then print (model.summary ()) output is as follows:
Here I will briefly around under the above code: When this layer as the first floor, a description input_shape
input_shape = (10, 32) is short 10 of the 32-dimensional vector, nb_filter: the number of dimensions of the convolution kernel, is output. filter_length: length of each filter.
First we look at the first convolution layer, the output shape is easy to understand, because there are 64 convolution kernel, so the output is 64, then we look argument: in fact can be understood, we have case (10, 2D convolution signal 32) is equivalent to the convolution 1D is subjected to convolution kernel (filter_length, 32) of the
Well, it sauce
Reproduced in: https: //www.cnblogs.com/qianboping/p/6516639.html
This article illustrates two things, one is Convolution1D introduction, the other is model.summary () is used.
First of all let me say at model.summary (), this method can print out the information model, readers can view the contents of each output.
Then say under Convolution1D uses, Convolution1D one-dimensional convolution, is mainly used to filter element adjacent one-dimensional input, official documents like this
keras.layers.convolutional.Convolution1D(nb_filter, filter_length, init='glorot_uniform', activation=None, weights=None, border_mode='valid', subsample_length=1, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None, input_length=None) Then the official gives examples of this is the
# apply a convolution 1d of length 3 to a sequence with 10 timesteps, # with 64 output filters model = Sequential() model.add(Convolution1D(64, 3, border_mode='same', input_shape=(10, 32))) # now model.output_shape == (None, 10, 64) # add a new conv1d on top model.add(Convolution1D(32, 3, border_mode='same')) # now model.output_shape == (None, 10, 32)
Then print (model.summary ()) output is as follows:
Here I will briefly around under the above code: When this layer as the first floor, a description input_shape
input_shape = (10, 32) is short 10 of the 32-dimensional vector, nb_filter: the number of dimensions of the convolution kernel, is output. filter_length: length of each filter.
First we look at the first convolution layer, the output shape is easy to understand, because there are 64 convolution kernel, so the output is 64, then we look argument: in fact can be understood, we have case (10, 2D convolution signal 32) is equivalent to the convolution 1D is subjected to convolution kernel (filter_length, 32) of the
Well, it sauce