momentum=0.9,
nesterovs_momentum=True,
power_t=0.5,
random_state=1,
shuffle=True,
solver='adam',
tol=0.0001,
validation_fraction=0.1,
verbose=False,
warm_start=True)
batches = iter_minibatches(sparse_matrix, prices, chunksize=1000)
count = 0
for X_chunk, y_chunk in batches:
print(count)
count += 1
if len(X_chunk) != 0:
neuralnet._partial_fit(X_chunk, y_chunk)
valmat = sparse_matrix[999999:].todense()
valprices = get_price_list(train)
print(valmat.shape)
print(valprices.shape)
predicted_prices = neuralnet.predict(valmat)
print('Prices predicted', time.time() - start)
print(valprices.shape)
print(predicted_prices.shape)
print("The score is:", calc_score(valprices, predicted_prices))
