def test_problem_1():
REF_XTRAIN = "fixtures/X_expected"
REF_YTRAIN = "fixtures/Y_expected"
(xtrain, ytrain) = hw1.load_data("data/digitstest.txt")
ref_xtrain = pickle.load(open(REF_XTRAIN, "rb"), **PICKLE_KWARGS)
ref_ytrain = pickle.load(open(REF_YTRAIN, "rb"), **PICKLE_KWARGS)
assert (ref_xtrain.shape == xtrain.shape)
assert (ref_ytrain.shape == ytrain.shape)
assert (isAllClose(ref_ytrain, ytrain))
assert (isAllClose(ref_xtrain, xtrain))
def test_problem_4():
STARTING_WEIGHTS_PATH = "fixtures/start_weights_problem4"
ENDING_WEIGHTS_PATH = "fixtures/final_weights_problem4"
STARTING_BIAS_PATH = "fixtures/start_bias_problem4"
ENDING_BIAS_PATH = "fixtures/final_bias_problem4"
PARAMS_PATH = "fixtures/problem4.params.json"
params = json.loads(open(PARAMS_PATH, "r").read())
(X, Y)= hw1.load_data("data/digitstrain.txt")
inputWeights = pickle.load(open(STARTING_WEIGHTS_PATH, "rb"), **PICKLE_KWARGS)
finalWeights = pickle.load(open(ENDING_WEIGHTS_PATH, "rb"), **PICKLE_KWARGS)
inputBias = pickle.load(open(STARTING_BIAS_PATH, "rb"), **PICKLE_KWARGS)
finalBias = pickle.load(open(ENDING_BIAS_PATH, "rb"), **PICKLE_KWARGS)
weightsToTest, biasToTest = hw1.update_weights_double_layer(X, Y, inputWeights, inputBias, float(params["LEARNING_RATE"]))
assert(isAllClose(finalWeights, weightsToTest))
assert(isAllClose(finalBias, biasToTest))
def test_problem_6():
STARTING_WEIGHTS_PATH = "fixtures/start_weights_problem6"
ENDING_WEIGHTS_PATH = "fixtures/final_weights_problem6"
STARTING_BIAS_PATH = "fixtures/start_bias_problem6"
ENDING_BIAS_PATH = "fixtures/final_bias_problem6"
PARAMS_PATH = "fixtures/problem6.params.json"
params = json.loads(open(PARAMS_PATH, "r").read())
(X, Y) = hw1.load_data("data/digitstrain.txt")
inputWeights = pickle.load(open(STARTING_WEIGHTS_PATH, "rb"),
**PICKLE_KWARGS)
finalWeights = pickle.load(open(ENDING_WEIGHTS_PATH, "rb"),
**PICKLE_KWARGS)
inputBias = pickle.load(open(STARTING_BIAS_PATH, "rb"), **PICKLE_KWARGS)
finalBias = pickle.load(open(ENDING_BIAS_PATH, "rb"), **PICKLE_KWARGS)
inputLoss, inputAccuracy = forward_and_compute_loss(
X, Y, inputWeights, inputBias, float(params["LEARNING_RATE"]),
params["ACTIVATION"])
weightsToTest, biasToTest = hw1.update_weights_double_layer_act_mom(
X, Y, inputWeights, inputBias, float(params["LEARNING_RATE"]),
params["ACTIVATION"], float(params["MOMENTUM"]),
int(params["EPOCH_COUNT"]))
correctLoss, correctAccuracy = forward_and_compute_loss(
X, Y, finalWeights, finalBias, float(params["LEARNING_RATE"]),
params["ACTIVATION"])
myLoss, myAccuracy = forward_and_compute_loss(
X, Y, weightsToTest, biasToTest, float(params["LEARNING_RATE"]),
params["ACTIVATION"])
print("inputLoss: ", inputLoss, " inputAccuracy: ", inputAccuracy)
print("correctLoss: ", correctLoss, " correctAccuracy: ", correctAccuracy)
print("myLoss: ", myLoss, " myAccuracy: ", myAccuracy)
assert (isAllClose(finalWeights, weightsToTest))
assert (isAllClose(finalBias, biasToTest))