def _make_model(opt, batch_shape):
ipt = Input(batch_shape=batch_shape)
x = Dense(batch_shape[-1])(ipt)
out = Dense(batch_shape[-1])(x)
model = Model(ipt, out)
model.compile(opt, 'mse')
return model
def _make_softmax_model():
ipt = Input(batch_shape=(batch_size, 8))
x = Dense(n_classes)(ipt)
out = Activation('softmax')(x)
model = Model(ipt, out)
model.compile('adam', 'categorical_crossentropy')
return model
def _make_multi_io_model():
ipt1 = Input((40, 8))
ipt2 = Input((40, 16))
ipts = concatenate([ipt1, ipt2])
out1 = GRU(6, return_sequences=True)(ipts)
out2 = GRU(12, return_sequences=True)(ipts)
model = Model([ipt1, ipt2], [out1, out2])
model.compile('adam', 'mse')
return model
def make_model(batch_shape, layer_kw={}):
"""Conv1D autoencoder"""
dim = batch_shape[-1]
bdim = dim // 2
ipt = Input(batch_shape=batch_shape)
x = Conv1D(dim, 8, activation='relu', **layer_kw)(ipt)
x = Conv1D(bdim, 1, activation='relu', **layer_kw)(x) # bottleneck
out = Conv1D(dim, 8, activation='linear', **layer_kw)(x)
model = Model(ipt, out)
model.compile('adam', 'mse')
return model
