mnist_clf.py

from ulab import numpy as np

##### weights sections starts #####
# Overwrite the variable declarations in mnist_clf.py with below declarations
# Once mnist_clf.py file is generated, create the mnist_clf.mpy using mpy-cross
# For more information visit:

weights_0 = [np.array([[ 0.27113578,  0.20170653,  0.17861792],
       [ 0.24902306,  0.13183539, -0.14804259],
       [-0.01049658,  0.02968406,  0.45212153]], dtype=np.float), np.array([[-0.09107354,  0.13827483, -0.01431286],
       [-0.26810253,  0.3130664 ,  0.354149  ],
       [ 0.3003585 , -0.1137372 ,  0.25937125]], dtype=np.float), np.array([[-0.5837926 , -0.638514  , -0.6892895 ],
       [ 0.18570413,  0.01022141, -0.2778978 ],
       [ 0.63264525,  0.5508433 ,  0.5610652 ]], dtype=np.float), np.array([[ 0.12367047, -0.01899654, -0.01016466],
       [-0.15401931,  0.1890724 , -0.2868167 ],
       [ 0.16170469,  0.39859372,  0.27118424]], dtype=np.float), np.array([[ 0.07503612,  0.00177742,  0.07363328],
       [-0.0482145 ,  0.33280817,  0.10877205],
       [ 0.32094854,  0.21895614,  0.05318537]], dtype=np.float), np.array([[ 0.35984838,  0.1353807 ,  0.21532203],
       [ 0.30407003,  0.341782  ,  0.39051265],
       [-0.34187052, -0.25696495,  0.28973493]], dtype=np.float), np.array([[ 0.37680805, -0.07486141, -0.22606412],
       [ 0.26239234,  0.22908504,  0.10255475],
       [-0.23665316, -0.11164176,  0.4089799 ]], dtype=np.float), np.array([[ 0.3795052 ,  0.03723119, -0.3509103 ],
       [ 0.3690208 ,  0.07578955, -0.492027  ],
       [ 0.54318553, -0.08572209, -0.6207421 ]], dtype=np.float)]
biases_0 = np.array([ 0.03325457,  0.0054133 ,  0.02119315,  0.04042089, -0.00086789,
        0.02445282,  0.06810823, -0.05000828], dtype=np.float)
weights_1 = [[np.array([[-0.24786572, -0.2586773 ,  0.14523607],
       [ 0.01356276,  0.14213222,  0.2358635 ],
       [ 0.09499675,  0.2471225 ,  0.08285091]], dtype=np.float), np.array([[-0.1369363 ,  0.02320191,  0.21658677],
       [ 0.2600999 ,  0.0863422 ,  0.06594379],
       [-0.05731571,  0.1658169 ,  0.27747446]], dtype=np.float), np.array([[-0.27961975, -0.7412398 , -0.45610505],
       [-0.29305872, -0.63865674, -0.67187315],
       [-0.4292236 , -0.68077034, -0.5687557 ]], dtype=np.float), np.array([[ 0.0513261 ,  0.12079202,  0.2950452 ],
       [ 0.24252512, -0.0358729 ,  0.28093854],
       [ 0.1428507 ,  0.13304752,  0.443847  ]], dtype=np.float), np.array([[-0.23965004,  0.13023156,  0.21294834],
       [ 0.12960428,  0.20961253,  0.09446213],
       [ 0.09460631,  0.32379478,  0.32922435]], dtype=np.float), np.array([[-0.02510539, -0.360277  , -0.00913496],
       [-0.23783284, -0.16562603, -0.21543421],
       [ 0.25506705,  0.08171264,  0.43940264]], dtype=np.float), np.array([[ 0.14738299, -0.25460106,  0.01186021],
       [-0.22123271,  0.13745707,  0.20470062],
       [ 0.31736043,  0.30726582,  0.29944462]], dtype=np.float), np.array([[0.65892726, 0.64775705, 0.6806931 ],
       [0.56258047, 0.84712416, 0.90248704],
       [0.28796187, 0.42817494, 0.74550325]], dtype=np.float)], [np.array([[ 0.04328201, -0.10109015,  0.22543822],
       [ 0.15367223,  0.00645262, -0.02850721],
       [ 0.20120633,  0.11375625, -0.23501655]], dtype=np.float), np.array([[ 0.12091237,  0.32103553,  0.07872619],
       [ 0.2313851 ,  0.24673115, -0.40749517],
       [-0.17387171,  0.05459687, -0.29864454]], dtype=np.float), np.array([[0.41561902, 0.19008869, 0.38777408],
       [0.8386257 , 0.38556382, 0.32245663],
       [0.04919922, 0.4175249 , 0.73300564]], dtype=np.float), np.array([[-0.15210186,  0.17434767,  0.00475235],
       [ 0.31125373,  0.33386433, -0.15916403],
       [ 0.12601072,  0.00840959,  0.11830918]], dtype=np.float), np.array([[ 0.16344619, -0.11252454, -0.11049405],
       [ 0.22664048,  0.10380711, -0.08260204],
       [-0.03484473,  0.15644014, -0.29968426]], dtype=np.float), np.array([[-0.06053404,  0.17621092,  0.16582003],
       [ 0.17382896,  0.1741999 , -0.01711291],
       [ 0.05790566,  0.33271536, -0.2979444 ]], dtype=np.float), np.array([[ 0.27746472,  0.00514999,  0.4094536 ],
       [ 0.01242439,  0.15310387,  0.16455957],
       [ 0.2069639 ,  0.42835537, -0.04344899]], dtype=np.float), np.array([[-0.40264997, -0.34848914, -0.44948712],
       [-0.46773008, -0.61715406, -0.02339443],
       [-0.38273665, -0.43446106, -0.27816382]], dtype=np.float)]]
biases_1 = np.array([-0.00466824,  0.06053527], dtype=np.float)
weights_2 = np.array([[-3.43542360e-02,  1.24569340e-02, -1.01025090e-01,
         1.95042178e-01, -9.46396962e-02, -1.72111675e-01,
         1.29697984e-02,  3.62256430e-02, -1.39920920e-01,
        -2.40619779e-01],
       [-2.63767503e-02,  3.59809309e-01, -4.26980713e-03,
         1.03359953e-01, -3.30028199e-02,  3.29381712e-02,
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        -2.48838197e-02],
       [-1.41832232e-02, -4.02720012e-02, -1.25228450e-01,
        -7.45609179e-02,  1.24849647e-01, -9.61621199e-03,
        -5.51049002e-02,  1.24971949e-01,  4.09637652e-02,
        -1.66859135e-01],
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         1.24852091e-01, -5.57627417e-02,  7.91337043e-02,
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        -6.86323419e-02],
       [-1.71364591e-01, -1.08315140e-01,  1.10668175e-01,
         7.20003173e-02, -9.66111477e-03, -8.51790830e-02,
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        -3.39450538e-02],
       [-1.65361345e-01, -2.66080745e-03, -1.54823616e-01,
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        -9.67597142e-02],
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       [-7.10577816e-02, -4.36927713e-02, -5.93490107e-03,
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        -1.16819739e-01,  8.67183283e-02, -1.70588076e-01,
        -8.47783536e-02],
       [-7.62927234e-02,  1.23997882e-01, -2.82910112e-02,
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        -4.75012287e-02, -2.99053729e-01, -8.27147886e-02,
         9.59107876e-02],
       [ 1.30051568e-01,  1.20422341e-01,  7.79150352e-02,
         1.59214631e-01, -2.09630102e-01, -1.26030892e-01,
        -3.60738307e-01,  1.89850450e-01, -3.42611186e-02,
         1.56964585e-01],
       [ 1.39969945e-01,  1.58272266e-01, -8.45177844e-02,
        -4.68299119e-03, -2.17619598e-01, -1.65681611e-03,
        -3.23734105e-01,  1.82670504e-01,  2.30724225e-03,
         7.41750933e-03],
       [-1.95183232e-01, -1.24143986e-02, -1.87648177e-01,
         7.51602501e-02,  1.06948055e-01, -1.05611362e-01,
        -1.13342799e-01,  3.86367261e-01, -1.51812896e-01,
         2.96252850e-03],
       [ 1.09679721e-01,  2.29673982e-01, -1.29075795e-01,
         7.93477669e-02, -6.09340854e-02, -1.42804042e-01,
        -9.65327024e-02,  3.73081304e-02, -9.08508822e-02,
        -8.97947624e-02],
       [-2.41425008e-01,  1.25623494e-01, -4.97053228e-02,
         6.20659068e-03, -4.92299236e-02, -4.58558574e-02,
        -6.33158386e-02,  2.31065989e-01, -1.99543580e-01,
         8.57211798e-02],
       [ 1.74285378e-02, -1.41780181e-02, -3.82782519e-02,
         3.25075053e-02, -2.77197752e-02,  9.90259424e-02,
         3.44947465e-02,  1.06476068e-01, -3.71126197e-02,
         7.55367428e-02],
       [-2.52962351e-01, -3.49975042e-02, -3.60676199e-01,
         8.14555511e-02,  1.49324656e-01, -1.89143375e-01,
        -2.17416836e-03,  1.98267445e-01, -2.74757683e-01,
         2.90630162e-01],
       [-1.46348134e-03, -1.49712175e-01, -1.70307145e-01,
         2.31748760e-01, -8.98532569e-02, -1.25951471e-03,
        -1.02555312e-01, -8.85091573e-02,  2.95067262e-02,
        -3.89914191e-03],
       [-2.46423170e-01, -1.65470038e-02, -3.24302405e-01,
         5.20454682e-02,  3.44794579e-02, -2.32065525e-02,
        -1.10184394e-01,  1.08168036e-01, -2.92988718e-01,
         1.51211485e-01],
       [-3.98818292e-02, -2.90472358e-01,  2.58128922e-02,
         9.53239128e-02, -2.02204376e-01,  7.07919821e-02,
        -7.70147741e-02, -1.71699598e-01,  1.81473494e-02,
        -3.60965542e-02],
       [-4.43426847e-01,  4.44332212e-02, -2.51465261e-01,
         5.09478971e-02, -1.05387755e-01,  3.67068499e-02,
        -3.90445679e-01,  7.60614052e-02, -1.43800214e-01,
         2.27002986e-03],
       [-9.43449438e-02, -3.03801656e-01, -1.68763787e-01,
         2.49296442e-01, -2.62488216e-01,  1.78874563e-02,
         1.37769938e-01, -1.55265793e-01,  1.25840306e-01,
        -8.86211395e-02],
       [-2.82146424e-01,  6.61316514e-02, -1.99185118e-01,
         2.04100043e-01, -7.62741193e-02,  1.56953916e-01,
        -4.38770473e-01,  2.03136921e-01, -1.39327794e-02,
         1.68202817e-01],
       [ 9.52909067e-02, -1.02897242e-01,  9.34488624e-02,
         7.43474588e-02, -1.43512025e-01,  2.07866162e-01,
        -1.02834001e-01, -4.92263399e-02,  1.43389329e-01,
        -2.73090005e-01],
       [-2.93373227e-01, -8.90307426e-02, -6.47801161e-02,
         1.37216210e-01,  7.58094192e-02,  8.99768174e-02,
        -4.77791965e-01,  2.72664666e-01, -1.15003452e-01,
         2.14276820e-01],
       [-5.44080697e-02,  1.14475153e-01,  1.61611736e-02,
        -6.92772865e-02,  3.64462547e-02, -3.64989638e-02,
        -9.32107940e-02,  7.75722787e-02,  5.75570390e-02,
        -1.33129790e-01],
       [-2.89876223e-01, -2.16837406e-01, -2.59444505e-01,
         1.02600686e-01,  1.28842527e-02,  7.34626353e-02,
        -3.15976351e-01,  2.83604831e-01,  2.12199409e-02,
         1.61198348e-01],
       [-7.68881887e-02,  1.63073331e-01,  1.82314500e-01,
        -8.92077535e-02, -8.22878480e-02, -6.74925521e-02,
         2.80383527e-02, -1.37540270e-02,  5.30623533e-02,
         5.17336838e-02],
       [-4.05993402e-01, -6.85865954e-02, -1.66067526e-01,
         9.04250890e-02, -1.10048600e-01,  6.50645569e-02,
        -1.11870870e-01,  8.98902416e-02,  3.75514105e-02,
         6.67748004e-02],
       [ 2.80667096e-02, -2.77854428e-02,  1.20992944e-01,
         9.94397700e-02, -1.25729665e-01,  6.41503409e-02,
        -1.13981906e-02, -1.90373793e-01, -1.07229203e-02,
         5.40791936e-02],
       [-4.12976176e-01, -7.56314583e-03, -7.77527615e-02,
        -3.59531902e-02, -1.61287203e-01,  1.45997986e-01,
        -2.51222014e-01,  8.93113762e-02,  8.54456499e-02,
         4.89056222e-02],
       [-1.51966527e-01, -1.88718647e-01,  1.02564216e-01,
        -9.41356868e-02,  2.92394659e-03, -5.37746660e-02,
        -2.07696512e-01, -1.89945437e-02,  2.43038312e-02,
         1.47700444e-01],
       [-3.82732332e-01,  1.56064080e-02, -4.47227843e-02,
        -6.65332079e-02,  1.18502369e-02,  1.90389782e-01,
        -2.77206212e-01, -1.35186156e-02, -1.64985642e-01,
         7.19405413e-02],
       [ 3.52253504e-02, -9.59177017e-02,  2.18709514e-01,
        -2.41258219e-01, -9.33201090e-02,  1.88166752e-01,
        -1.44433364e-01, -8.59880596e-02, -4.63345408e-01,
         1.02791220e-01]], dtype=np.float)
biases_2 = np.array([-0.03211425,  0.15643457,  0.03762675, -0.11051846, -0.06225784,
        0.21276157, -0.02758044,  0.17946692, -0.3119275 , -0.01637945],
      dtype=np.float)

##### weights sections ends #####


##### validation sections starts #####
# Results of predictions

result_0 = [2.066384240606567e-06, 0.060212261974811554, 0.054556529968976974, 0.008784947916865349, 0.000567541632335633, 0.013615792617201805, 5.976201755686361e-10, 0.8617925047874451, 0.00046830205246806145, 8.883725399755349e-08]

result_1 = [1.9689446162374225e-07, 0.09803207218647003, 0.0013439618051052094, 0.00024759149528108537, 0.004475444555282593, 0.00023588018666487187, 7.59949655287917e-12, 0.8954958319664001, 0.0001691034558461979, 2.585356151385554e-09]

result_2 = [2.066384240606567e-06, 0.060212261974811554, 0.054556529968976974, 0.008784947916865349, 0.000567541632335633, 0.013615792617201805, 5.976201755686361e-10, 0.8617925047874451, 0.00046830205246806145, 8.883725399755349e-08]

result_3 = [6.185747747355919e-11, 2.635237478898489e-06, 1.1324819906538397e-19, 3.4043805113798868e-18, 0.9999973773956299, 2.1219048715124806e-20, 9.49689477991282e-14, 9.533889233637183e-09, 6.001739660810301e-10, 3.215417027687545e-14]


##### validation sections ends #####


def relu(x):
    return np.maximum(0, x)


def convolve_2d_layer(image, filter, bias):
    output_height = image.shape[0] - filter.shape[0] + 1
    output_width = image.shape[1] - filter.shape[1] + 1
    output = np.zeros((output_height, output_width))
    for i in range(output_height):
        for j in range(output_width):
            conv_sum = np.sum(image[i : i + 3, j : j + 3] * filter) + bias
            output[i, j] = relu(conv_sum)

    return output


def max_pool_2d(input_array, pool_size):
    output_height = input_array.shape[0] // pool_size
    output_width = input_array.shape[1] // pool_size
    output = np.zeros((output_height, output_width))

    for i in range(output_height):
        for j in range(output_width):
            pool_region = input_array[
                i * pool_size : (i + 1) * pool_size, j * pool_size : (j + 1) * pool_size
            ]
            output[i, j] = np.max(pool_region)

    return output


def flatten_and_concatenate(array1, array2):
    flattened_array1 = array1.flatten()
    flattened_array2 = array2.flatten()

    flattened_combined_array = np.concatenate((flattened_array1, flattened_array2))
    return flattened_combined_array


def interleave_arrays(array1, array2):
    flattened_array1 = array1.flatten()
    flattened_array2 = array2.flatten()

    interleaved_array = np.zeros(
        len(flattened_array1) + len(flattened_array2), dtype=flattened_array1.dtype
    )
    interleaved_array[0::2] = flattened_array1
    interleaved_array[1::2] = flattened_array2

    return interleaved_array


def dense_layer(input_vector, weights, biases):
    output = np.dot(input_vector, weights) + biases
    return output


def softmax(x):
    x -= np.max(x)
    exp_x = np.exp(x)
    return exp_x / np.sum(exp_x)


def predict(image):
    layer_1_output = []
    for i in range(0, len(weights_0)):
        x = convolve_2d_layer(image, weights_0[i], biases_0[i])
        layer_1_output.append(x)

    layer_2_output = []
    for i in range(0, len(layer_1_output)):
        x = max_pool_2d(layer_1_output[i], 2)
        layer_2_output.append(x)

    layer_3_output = []
    for filter_len in range(0, 2):  # 2 times
        output = np.zeros((12, 12))
        for i in range(12):
            for j in range(12):
                op_channel = np.zeros((3, 3))
                for channel in range(0, 8):
                    x = (
                        layer_2_output[channel][i : i + 3, j : j + 3]
                        * weights_1[filter_len][channel]
                    )
                    op_channel = op_channel + x

                conv_sum = np.sum(
                    op_channel
                )  
                output[i, j] = relu(conv_sum + biases_1[filter_len])
        layer_3_output.append(output)

    layer_4_input = interleave_arrays(
        layer_3_output[0], layer_3_output[1]
    )  
    layer_4_output = dense_layer(layer_4_input, weights_2, biases_2)
    layer_5_output = softmax(layer_4_output)
    arg_max = np.argmax(layer_5_output)
    return arg_max, layer_5_output[arg_max],layer_5_output

def validate():
    patterns = [
        np.array([[1, 0] * 15] * 30),
        np.ones((30, 30)),
        np.array([[0, 1] * 15] * 30),
        np.array([[1, 1, 0, 0] * 7 + [1, 1]] * 30),
    ]

    results = [result_0, result_1, result_2, result_3]

    for i in range(len(patterns)):
        expected_result = results[i]
        pattern = patterns[i]
        output = predict(pattern)[2]

        output_equal = True
        for j in range(len(output)):
            if abs(output[j] - expected_result[j]) > 0.00001:
                output_equal = False
                break

        assert output_equal, f"Validation failed for result_{i}, {output},{expected_result}"

    return True