def test_Exp1D_double(self): npr = np.exp(self.np_double_a) dcr = dc.exp(self.dc_double_a) np.testing.assert_allclose(npr, np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3)
def test_Exp2D_double_2(self): np_double_a = np.reshape(self.np_double_a, (6, 8)) dc_double_a = dc.reshape(self.dc_double_a, (6, 8)) npr = np.exp(np_double_a) dcr = dc.exp(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3)
def test_Exp2D_float_3(self): np_float_a = np.reshape(self.np_float_a, (12, 4)) dc_float_a = dc.reshape(self.dc_float_a, (12, 4)) npr = np.exp(np_float_a) dcr = dc.exp(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3)
result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True, shell=True) for line in result.stdout.split("\n"): if (line.find("writing file ") == 0): resultFile = line[13:line.find('.', -1)].split()[0] with open(resultFile, 'r') as f: return f.read() return "" # Run model in the loop import deepC.dnnc as dc for i in range(5): index = random.randint(0, len(images) - 1) write_image(index) model_result = run_model("./mnist.exe ./image.data").strip("[]") # Convert log softmax output to probability log_probs = dc.array([float(f) for f in model_result.strip("[]").split()]) probabilities = dc.exp(log_probs) trueLabel = labels[index] prediction = dc.argmax(probabilities)[0] display(images[index]) print("True label = ", labels[index]) print("Model Prediction: ", dc.argmax(probabilities))