Created
June 5, 2017 18:33
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SFC Layers Example: Basset Model with SFC Layer Setup
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| #install keras from https://github.com/kundajelab/keras/tree/keras_1 | |
| from __future__ import print_function | |
| import keras | |
| import numpy as np | |
| np.random.seed(1) | |
| #build a model | |
| model = keras.models.Sequential() | |
| model.add(keras.layers.convolutional.Convolution1D(input_shape=(1000,4), | |
| nb_filter=300, | |
| filter_length=19)) | |
| model.add(keras.layers.normalization.BatchNormalization()) | |
| model.add(keras.layers.core.Activation("relu")) | |
| model.add(keras.layers.pooling.MaxPooling1D(pool_length=3, stride=3)) | |
| model.add(keras.layers.convolutional.Convolution1D(nb_filter=200, | |
| filter_length=11)) | |
| model.add(keras.layers.normalization.BatchNormalization()) | |
| model.add(keras.layers.core.Activation("relu")) | |
| model.add(keras.layers.pooling.MaxPooling1D(pool_length=4, stride=4)) | |
| model.add(keras.layers.convolutional.Convolution1D(nb_filter=200, | |
| filter_length=7)) | |
| model.add(keras.layers.normalization.BatchNormalization()) | |
| model.add(keras.layers.core.Activation("relu")) | |
| model.add(keras.layers.pooling.MaxPooling1D(pool_length=4, stride=4)) | |
| model.add(keras.layers.convolutional.SeparableFC(symmetric=True, | |
| smoothness_second_diff=True, | |
| output_dim=1000, | |
| smoothness_penalty=10.0, | |
| smoothness_l1=True)) | |
| model.add(keras.layers.core.Dense(output_dim=1000)) | |
| model.add(keras.layers.core.Activation("relu")) | |
| model.add(keras.layers.core.Dropout(0.3)) | |
| model.add(keras.layers.core.Dense(output_dim=1000)) | |
| model.add(keras.layers.core.Activation("relu")) | |
| model.add(keras.layers.core.Dropout(0.3)) | |
| model.add(keras.layers.core.Dense(output_dim=10)) | |
| model.add(keras.layers.core.Activation("sigmoid")) | |
| model.compile(optimizer="Adam", loss="binary_crossentropy") | |
| #randomly generate some inputs and get predictions | |
| rand_inp = np.random.random((10, 1000, 4)) | |
| predict = model.predict(rand_inp) |
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