look_back = 3
num_features =
nb_samples = X_train.shape[0] - look_back
x_train_reshaped = np.zeros((nb_samples, look_back, num_features))
y_train_reshaped = np.zeros((nb_samples))
for i in range(nb_samples):
y_position = i + look_back
x_train_reshaped[i] = X_train[i:y_position]
y_train_reshaped[i] = y_train[y_position]
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(units, activation='relu'))
# model.add(tf.keras.layers.Embedding(len(test_encoding), 64))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))
# model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))
# One or more dense layers.
# Edit the list in the `for` line to experiment with layer sizes.
for units in [64, 64]:
model.add(tf.keras.layers.Dense(units, activation='relu'))
# Output layer. The first argument is the number of labels.
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy', tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),