test.py

#from: t81_558_class_10_2_lstm.ipynb (Keras and Tensorflow version)

#from tensorflow.keras.preprocessing import sequence
#from tensorflow.keras.models import Sequential
#from tensorflow.keras.layers import Dense, Embedding
#from tensorflow.keras.layers import LSTM

import keras
import numpy as np

max_features = 4 # 0,1,2,3 (total of 4)
x = [
    [[0],[1],[1],[0],[0],[0]],
    [[0],[0],[0],[2],[2],[0]],
    [[0],[0],[0],[0],[3],[3]],
    [[0],[2],[2],[0],[0],[0]],
    [[0],[0],[3],[3],[0],[0]],
    [[0],[0],[0],[0],[1],[1]]
]
x = np.array(x,dtype=np.float32)
y = np.array([1,2,3,2,3,1],dtype=np.int32)

# Convert y2 to dummy variables
y2 = np.zeros((y.shape[0], max_features),dtype=np.float32)
y2[np.arange(y.shape[0]), y] = 1.0
print(y2)

print('Build model...')
model = keras.Sequential()
model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2, \
               input_shape=(None, 1)))
model.add(keras.layers.Dense(4, activation='sigmoid'))

# try using different optimizers and different optimizer configs
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

print('Train...')
model.fit(x,y2,epochs=200)
pred = model.predict(x)
predict_classes = np.argmax(pred,axis=1)
print("Predicted classes: {}",predict_classes)
print("Expected classes: {}",y)