data = (data - min_val) / (max_val - min_val)
# split into train and test sets
split = int(len(data) * 0.70)
train = data[:split]
test = data[split:]
trainX, trainY = create_dataset(train)
testX, testY = create_dataset(test)
trainX = trainX[:, :, np.newaxis]
testX = testX[:, :, np.newaxis]
# create and fit the RNN
model = Sequential()
model.add(CuDNNLSTM(15, input_shape=(
config.look_back,
1))) #first parameter represents the number of blocks that are remembered
model.add(Dense(1))
model.compile(loss='mae', optimizer='rmsprop')
model.fit(trainX,
trainY,
epochs=1000,
batch_size=40,
validation_data=(testX, testY),
callbacks=[
PlotCallback(trainX, trainY, testX, testY, config.look_back,
config.repeated_predictions),
WandbCallback()
])
# normalize data to between 0 and 1
max_val = max(data)
min_val = min(data)
data = (data - min_val) / (max_val - min_val)
# split into train and test sets
split = int(len(data) * 0.70)
train = data[:split]
test = data[split:]
trainX, trainY = create_dataset(train)
testX, testY = create_dataset(test)
trainX = trainX[:, :, np.newaxis]
testX = testX[:, :, np.newaxis]
# create and fit the RNN
model = Sequential()
model.add(SimpleRNN(1, input_shape=(config.look_back, 1)))
model.compile(loss='mae', optimizer='adam')
model.fit(trainX,
trainY,
epochs=1000,
batch_size=1,
validation_data=(testX, testY),
callbacks=[
WandbCallback(),
PlotCallback(trainX, trainY, testX, testY, config.look_back)
])
data = load_data("sin")
# normalize data to between 0 and 1
max_val = max(data)
min_val = min(data)
data=(data-min_val)/(max_val-min_val)
# split into train and test sets
split = int(len(data) * 0.70)
train = data[:split]
test = data[split:]
trainX, trainY = create_dataset(train)
testX, testY = create_dataset(test)
trainX = trainX[:, :, np.newaxis]
testX = testX[:, :, np.newaxis]
# create and fit the RNN
model = Sequential()
model.add(SimpleRNN(10, input_shape=(config.look_back,1 )))
model.add(Dense(1))
model.compile(loss='mae', optimizer='rmsprop')
model.fit(trainX, trainY, epochs=1000, batch_size=20, validation_data=(testX, testY), callbacks=[WandbCallback(), PlotCallback(trainX, trainY, testX, testY, config.look_back)])
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae_mlp')
models = (encoder, decoder)
data = (x_test, y_test)
reconstruction_loss = binary_crossentropy(inputs, outputs)
reconstruction_loss *= original_dim
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
vae.fit(x_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, None),
callbacks=[
WandbCallback(),
PlotCallback(encoder, decoder, (x_test, y_test))
])
vae.save_weights('vae_mlp_mnist.h5')
dataY.append(dataset[i + config.look_back])
return np.array(dataX), np.array(dataY)
# normalize data to between 0 and 1
#max_val = max(data)
#min_val = min(data)
#data=(data-min_val)/(max_val-min_val)
# split into train and test sets
split = int(len(data) * 0.70)
train = data[:split]
test = data[split:]
trainX, trainY = create_dataset(train)
testX, testY = create_dataset(test)
trainX = trainX[:, :, np.newaxis]
testX = testX[:, :, np.newaxis]
# create and fit the RNN
model = Sequential()
model.add(SimpleRNN(1, input_shape=(config.look_back,1 )))
model.compile(loss='mae', optimizer='adam', metrics=['mae'])
model.fit(trainX, trainY, epochs=1000, batch_size=1, validation_data=(testX, testY), callbacks=[WandbCallback(), PlotCallback(trainX, trainY, testX, testY, config.look_back, config.repeated_predictions)])