Datasets:
HenryAI
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KerasCodeExamples.txt

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x = data[:, 1:]
return x, y.astype(int)
root_url = \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\"
x_train, y_train = readucr(root_url + \"FordA_TRAIN.tsv\")
x_test, y_test = readucr(root_url + \"FordA_TEST.tsv\")
Visualize the data
Here we visualize one timeseries example for each class in the dataset.
classes = np.unique(np.concatenate((y_train, y_test), axis=0))
plt.figure()
for c in classes:
c_x_train = x_train[y_train == c]
plt.plot(c_x_train[0], label=\"class \" + str(c))
plt.legend(loc=\"best\")
plt.show()
plt.close()
png
Standardize the data
Our timeseries are already in a single length (500). However, their values are usually in various ranges. This is not ideal for a neural network; in general we should seek to make the input values normalized. For this specific dataset, the data is already z-normalized: each timeseries sample has a mean equal to zero and a standard deviation equal to one. This type of normalization is very common for timeseries classification problems, see Bagnall et al. (2016).
Note that the timeseries data used here are univariate, meaning we only have one channel per timeseries example. We will therefore transform the timeseries into a multivariate one with one channel using a simple reshaping via numpy. This will allow us to construct a model that is easily applicable to multivariate time series.
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
Finally, in order to use sparse_categorical_crossentropy, we will have to count the number of classes beforehand.
num_classes = len(np.unique(y_train))
Now we shuffle the training set because we will be using the validation_split option later when training.
idx = np.random.permutation(len(x_train))
x_train = x_train[idx]
y_train = y_train[idx]
Standardize the labels to positive integers. The expected labels will then be 0 and 1.
y_train[y_train == -1] = 0
y_test[y_test == -1] = 0
Build a model
We build a Fully Convolutional Neural Network originally proposed in this paper. The implementation is based on the TF 2 version provided here. The following hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using KerasTuner.
def make_model(input_shape):
input_layer = keras.layers.Input(input_shape)
conv1 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(input_layer)
conv1 = keras.layers.BatchNormalization()(conv1)
conv1 = keras.layers.ReLU()(conv1)
conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv1)
conv2 = keras.layers.BatchNormalization()(conv2)
conv2 = keras.layers.ReLU()(conv2)
conv3 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv2)
conv3 = keras.layers.BatchNormalization()(conv3)
conv3 = keras.layers.ReLU()(conv3)
gap = keras.layers.GlobalAveragePooling1D()(conv3)
output_layer = keras.layers.Dense(num_classes, activation=\"softmax\")(gap)
return keras.models.Model(inputs=input_layer, outputs=output_layer)
model = make_model(input_shape=x_train.shape[1:])
keras.utils.plot_model(model, show_shapes=True)
('Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.')
Train the model
epochs = 500
batch_size = 32
callbacks = [
keras.callbacks.ModelCheckpoint(
\"best_model.h5\", save_best_only=True, monitor=\"val_loss\"
),
keras.callbacks.ReduceLROnPlateau(
monitor=\"val_loss\", factor=0.5, patience=20, min_lr=0.0001
),
keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),
]
model.compile(
optimizer=\"adam\",
loss=\"sparse_categorical_crossentropy\",
metrics=[\"sparse_categorical_accuracy\"],
)
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_split=0.2,
verbose=1,
)
Epoch 1/500
90/90 [==============================] - 1s 8ms/step - loss: 0.5531 - sparse_categorical_accuracy: 0.7017 - val_loss: 0.7335 - val_sparse_categorical_accuracy: 0.4882
Epoch 2/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4520 - sparse_categorical_accuracy: 0.7729 - val_loss: 0.7446 - val_sparse_categorical_accuracy: 0.4882