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 an...
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 ...
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