signals_train = signals_train.reshape(
(-1, signals_train.shape[2], 1)) #(179,18,1024,1)
signals_test = signals_test.reshape((-1, signals_test.shape[2], 1))
# signals_train = signals_train.reshape((signals_train.shape[0], signals_train.shape[1], signals_train.shape[2], 1)) #(179,18,1024,1)
# signals_test = signals_test.reshape((signals_test.shape[0], signals_test.shape[1], signals_test.shape[2], 1))
print(signals_test.shape)
# print(signals_train[0,0])
# plt.imshow(signals_train[0,0], cmap=plt.cm.Reds)
# plt.show()
# plt.plot(signals_train[0,0])
# plt.show()
# exit()
# Load, Reshape and Binarize labels
labels_train = LabelEncoder().fit_transform(labels_train.flatten())
labels_test = LabelEncoder().fit_transform(labels_test.flatten())
print(labels_train.shape)
print(labels_test.shape)
model = Autoencoder()
model.fit(signals_train,
n_epochs=1,
learning_rate=0.003,
batch_size=64,
load=False,
save=True,
name='1/CAE_class')
# Best: CAE4.2 (256,128);CAE4.2.ld (4,1);
'https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data',
header=None)
df.columns = [
'buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'target'
]
df.head()
from sklearn.preprocessing import LabelEncoder
# Get the feature vector
# Implement me
X = df.iloc[:, :-1].values
# Encode the feature vector using one-hot-encoding
# Implement me
X = LabelEncoder().fit_transform(X.flatten()).reshape(X.shape)
# Get the target vector
# Implement me
y = df['target'].values
# Encode the target vector
# Implement me
le = LabelEncoder()
y = le.fit_transform(y)
# Randomly choose 30% of the data for testing (set randome_state as 0 and stratify as y)
# Implement me
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
