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Conv1D model for time series
2019 Community Moderator ElectionHow to get file creation & modification date/times in Python?How to get the current time in PythonHow can I make a time delay in Python?How do I get time of a Python program's execution?Measure time elapsed in Python?Cannot make this autoencoder network function properly (with convolutional and maxpool layers)Keras Conv1D for Time SeriesKeras-conv1d for Time series for imbalanced time series ClassificationHow to setup 1D-Convolution and LSTM in KerasKeras Conv1D on Multiple Time Series : One at a time
I am a novice in the area of Deep Learning and am willing to build a Conv1D autoencoder for time series with such shapes:
- samples: 200
- timesteps: 23
- features: 178
I am not sure about how should I set the parameters: filters, kernel_size and the layers MaxPooling1D(what's the behaviour?), Flatten(what's the behaviour?), and how should be architecturally designed the simplest Conv1D Autoencoder.
I tried to gather something alone but my version is not working at all:
input_layer = Input(shape=(TIMESTEPS, feature_size))
# ENCODER
x = Conv1D(filters=encoding_dim, kernel_size=TIMESTEPS, activation='relu', padding='valid')(input_layer)
x1 = MaxPooling1D(poolsize=TIMESTEPS)(x)
flat = Flatten()(x1)
encoded = Dense(units=encoding_dim, activation = 'relu')(flat)
print("shape of encoded ".format(K.int_shape(encoded)))
# DECODER
x_ = Conv1D(encoding_dim, TIMESTEPS, activation='relu', padding='valid')(encoded)
upsamp = UpSampling1D(TIMESTEPS)(x_)
flat = Flatten()(upsamp)
decoded = Dense(units=feature_size, activation = 'relu')(flat)
decoded = Reshape((TIMESTEPS, feature_size))(decoded)
print("shape of decoded ".format(K.int_shape(decoded)))
autoencoder = Model(input_layer, decoded)
ValueError: Negative dimension size caused by subtracting 23 from 1 for 'max_pooling1d/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,59].
python keras conv-neural-network autoencoder
asked Mar 6 at 14:24
GuidoGuido
678
add a comment |
I am a novice in the area of Deep Learning and am willing to build a Conv1D autoencoder for time series with such shapes:
- samples: 200
- timesteps: 23
- features: 178
I am not sure about how should I set the parameters: filters, kernel_size and the layers MaxPooling1D(what's the behaviour?), Flatten(what's the behaviour?), and how should be architecturally designed the simplest Conv1D Autoencoder.
I tried to gather something alone but my version is not working at all:
input_layer = Input(shape=(TIMESTEPS, feature_size))
# ENCODER
x = Conv1D(filters=encoding_dim, kernel_size=TIMESTEPS, activation='relu', padding='valid')(input_layer)
x1 = MaxPooling1D(poolsize=TIMESTEPS)(x)
flat = Flatten()(x1)
encoded = Dense(units=encoding_dim, activation = 'relu')(flat)
print("shape of encoded ".format(K.int_shape(encoded)))
# DECODER
x_ = Conv1D(encoding_dim, TIMESTEPS, activation='relu', padding='valid')(encoded)
upsamp = UpSampling1D(TIMESTEPS)(x_)
flat = Flatten()(upsamp)
decoded = Dense(units=feature_size, activation = 'relu')(flat)
decoded = Reshape((TIMESTEPS, feature_size))(decoded)
print("shape of decoded ".format(K.int_shape(decoded)))
autoencoder = Model(input_layer, decoded)
ValueError: Negative dimension size caused by subtracting 23 from 1 for 'max_pooling1d/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,59].
python keras conv-neural-network autoencoder
asked Mar 6 at 14:24
GuidoGuido
678
add a comment |
I am a novice in the area of Deep Learning and am willing to build a Conv1D autoencoder for time series with such shapes:
- samples: 200
- timesteps: 23
- features: 178
I am not sure about how should I set the parameters: filters, kernel_size and the layers MaxPooling1D(what's the behaviour?), Flatten(what's the behaviour?), and how should be architecturally designed the simplest Conv1D Autoencoder.
I tried to gather something alone but my version is not working at all:
input_layer = Input(shape=(TIMESTEPS, feature_size))
# ENCODER
x = Conv1D(filters=encoding_dim, kernel_size=TIMESTEPS, activation='relu', padding='valid')(input_layer)
x1 = MaxPooling1D(poolsize=TIMESTEPS)(x)
flat = Flatten()(x1)
encoded = Dense(units=encoding_dim, activation = 'relu')(flat)
print("shape of encoded ".format(K.int_shape(encoded)))
# DECODER
x_ = Conv1D(encoding_dim, TIMESTEPS, activation='relu', padding='valid')(encoded)
upsamp = UpSampling1D(TIMESTEPS)(x_)
flat = Flatten()(upsamp)
decoded = Dense(units=feature_size, activation = 'relu')(flat)
decoded = Reshape((TIMESTEPS, feature_size))(decoded)
print("shape of decoded ".format(K.int_shape(decoded)))
autoencoder = Model(input_layer, decoded)
ValueError: Negative dimension size caused by subtracting 23 from 1 for 'max_pooling1d/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,59].
python keras conv-neural-network autoencoder
asked Mar 6 at 14:24
GuidoGuido
678
I am a novice in the area of Deep Learning and am willing to build a Conv1D autoencoder for time series with such shapes:
- samples: 200
- timesteps: 23
- features: 178
I am not sure about how should I set the parameters: filters, kernel_size and the layers MaxPooling1D(what's the behaviour?), Flatten(what's the behaviour?), and how should be architecturally designed the simplest Conv1D Autoencoder.
I tried to gather something alone but my version is not working at all:
input_layer = Input(shape=(TIMESTEPS, feature_size))
# ENCODER
x = Conv1D(filters=encoding_dim, kernel_size=TIMESTEPS, activation='relu', padding='valid')(input_layer)
x1 = MaxPooling1D(poolsize=TIMESTEPS)(x)
flat = Flatten()(x1)
encoded = Dense(units=encoding_dim, activation = 'relu')(flat)
print("shape of encoded ".format(K.int_shape(encoded)))
# DECODER
x_ = Conv1D(encoding_dim, TIMESTEPS, activation='relu', padding='valid')(encoded)
upsamp = UpSampling1D(TIMESTEPS)(x_)
flat = Flatten()(upsamp)
decoded = Dense(units=feature_size, activation = 'relu')(flat)
decoded = Reshape((TIMESTEPS, feature_size))(decoded)
print("shape of decoded ".format(K.int_shape(decoded)))
autoencoder = Model(input_layer, decoded)
ValueError: Negative dimension size caused by subtracting 23 from 1 for 'max_pooling1d/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,59].
python keras conv-neural-network autoencoder
python keras conv-neural-network autoencoder
asked Mar 6 at 14:24
GuidoGuido
678
asked Mar 6 at 14:24
GuidoGuido
678
asked Mar 6 at 14:24
GuidoGuido
678
asked Mar 6 at 14:24
GuidoGuido
678
asked Mar 6 at 14:24
GuidoGuido
678
678
add a comment |
add a comment |
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