n_chars = len(alphabet)
n_in_seq_length = n_numbers * ceil(log10(largest + 1)) + n_numbers - 1
n_out_seq_length = ceil(log10(n_numbers * (largest + 1)))
n_batch = 100
n_epoch = 500
model = Sequential([
LSTM(100, input_shape=(n_in_seq_length, n_chars)),
RepeatVector(n_out_seq_length),
LSTM(50, return_sequences=True),
TimeDistributed(Dense(n_chars, activation='softmax'))
])
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
for i in range(n_epoch):
x, y = generate_data(n_samples, largest, alphabet)
model.fit(x, y, epochs=1, batch_size=n_batch)
model.save('training/keras_classifier.h5')
# evaluate on some new patterns
x, y = generate_data(n_samples, largest, alphabet)
result = model.predict(x, batch_size=n_batch, verbose=0)
# calculate error
expected = [invert(x, alphabet) for x in y]
predicted = [invert(x, alphabet) for x in result]
# show some examples
for i in range(20):
print('Expected=%s, Predicted=%s' % (expected[i], predicted[i]))
n_out_seq_length = ceil(log10(numbers * (largest + 1)))
model = Sequential([
LSTM(100, input_shape=(converter.max_number_length, num_chars)),
RepeatVector(n_out_seq_length),
LSTM(50, return_sequences=True),
TimeDistributed(Dense(1, activation='softmax'))
])
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
for i in range(epoch):
x, y = generator.build_sample(samples, largest)
print(converter.max_number_length, num_chars)
print(len(x[0]), len(x[0][0]))
model.fit(x, y, epochs=1, batch_size=batch)
model.save('training/classifier.h5')
# evaluate on some new patterns
x, y = generator.build_sample(samples, largest)
result = model.predict(x, batch_size=batch, verbose=0)
# calculate error
expected = [converter.invert(x) for x in y]
predicted = [converter.invert(x) for x in result]
# show some examples
for i in range(20):
print('Expected=%s, Predicted=%s' % (expected[i], predicted[i]))