In [1]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile

cifar10_dataset_folder_path = 'cifar-10-batches-py'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile('cifar-10-python.tar.gz'):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
        urlretrieve(
            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
            'cifar-10-python.tar.gz',
            pbar.hook)

if not isdir(cifar10_dataset_folder_path):
    with tarfile.open('cifar-10-python.tar.gz') as tar:
        tar.extractall()
        tar.close()


tests.test_folder_path(cifar10_dataset_folder_path)


    

    




All files found!

In [2]:

    

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import helper
import numpy as np
import matplotlib.pyplot as plt

# Explore the dataset
batch_id = 1
sample_id = 5
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)


    

    




Stats of batch 1:
Samples: 10000
Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]

Example of Image 5:
Image - Min Value: 0 Max Value: 252
Image - Shape: (32, 32, 3)
Label - Label Id: 1 Name: automobile






    

In [3]:

    

#histogram of labels in batch
features, labels = helper.load_cfar10_batch(cifar10_dataset_folder_path, batch_id)
plt.hist(labels)
plt.show()
#labels seem to be evenly distributed and not in order. The shape of the images is 32x32x3


    

In [4]:

    

def normalize(x):
    """
    Normalize a list of sample image data in the range of 0 to 1
    : x: List of image data.  The image shape is (32, 32, 3)
    : return: Numpy array of normalize data
    """
    # TODO: Implement Function
    print((x-x.min())/(x.max()-x.min()))
    return (x-x.min())/(x.max()-x.min())


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)


    

    




[[[[ 0.10980392  0.45490196  0.5372549 ]
   [ 0.44313725  0.06666667  0.38823529]
   [ 0.11764706  0.51764706  0.21960784]
   ..., 
   [ 0.42745098  0.02745098  0.6       ]
   [ 0.49803922  0.39215686  0.36470588]
   [ 0.2745098   0.04313725  0.43529412]]

  [[ 0.76470588  0.31372549  0.5372549 ]
   [ 0.90196078  0.29803922  0.72156863]
   [ 0.94509804  0.92156863  0.00784314]
   ..., 
   [ 0.69019608  0.95686275  0.63137255]
   [ 0.36078431  0.17647059  0.45882353]
   [ 0.15294118  0.45098039  0.54901961]]

  [[ 0.78039216  0.58039216  0.62745098]
   [ 0.23137255  0.39607843  0.71764706]
   [ 0.2         0.2         0.16470588]
   ..., 
   [ 0.00392157  0.29019608  0.58039216]
   [ 0.53333333  0.98823529  0.45490196]
   [ 0.04705882  0.14901961  0.1372549 ]]

  ..., 
  [[ 0.11764706  0.03529412  0.54901961]
   [ 0.28627451  0.65098039  0.18823529]
   [ 0.36470588  0.36470588  0.19607843]
   ..., 
   [ 0.51372549  0.84705882  0.32156863]
   [ 0.38039216  0.61176471  0.80784314]
   [ 0.74117647  0.40392157  0.8745098 ]]

  [[ 0.85490196  0.04705882  0.40784314]
   [ 0.5372549   0.75294118  0.19215686]
   [ 0.70196078  0.65490196  0.83529412]
   ..., 
   [ 0.58039216  0.43921569  0.8       ]
   [ 0.70980392  0.45490196  0.64705882]
   [ 0.78823529  0.56470588  0.34117647]]

  [[ 0.43529412  0.15686275  0.54509804]
   [ 0.01568627  0.49411765  0.42352941]
   [ 0.27843137  0.85098039  0.05882353]
   ..., 
   [ 0.19607843  0.55686275  1.        ]
   [ 0.78039216  0.94901961  0.99215686]
   [ 0.61176471  0.92941176  0.18823529]]]


 [[[ 0.78039216  0.61176471  0.53333333]
   [ 0.01176471  0.39215686  0.83921569]
   [ 0.78431373  0.46666667  0.50588235]
   ..., 
   [ 0.11764706  0.60392157  0.43137255]
   [ 0.35686275  0.38431373  0.17647059]
   [ 0.19607843  0.34509804  0.58039216]]

  [[ 0.39607843  0.79215686  0.98823529]
   [ 0.32156863  0.93333333  0.42745098]
   [ 0.18823529  0.7372549   0.27058824]
   ..., 
   [ 0.4627451   0.54117647  0.72156863]
   [ 0.41960784  0.30980392  0.87843137]
   [ 0.38039216  0.2627451   0.96078431]]

  [[ 0.31764706  0.91372549  0.43921569]
   [ 0.87843137  0.38039216  0.45098039]
   [ 0.83137255  0.00392157  0.22745098]
   ..., 
   [ 0.24705882  0.19215686  0.80392157]
   [ 0.87058824  0.9372549   0.2       ]
   [ 0.65490196  0.91372549  0.85490196]]

  ..., 
  [[ 0.45882353  0.03137255  0.48627451]
   [ 0.0627451   0.82352941  0.25882353]
   [ 0.50980392  0.91764706  0.28235294]
   ..., 
   [ 0.4627451   0.39215686  0.57254902]
   [ 0.56078431  0.36078431  0.97647059]
   [ 0.10196078  0.47843137  0.37647059]]

  [[ 0.41960784  0.97254902  0.85490196]
   [ 0.25098039  0.5372549   0.41176471]
   [ 0.02352941  0.66666667  0.94901961]
   ..., 
   [ 0.03137255  0.08235294  0.49019608]
   [ 0.85882353  0.24705882  0.18823529]
   [ 0.01568627  0.56470588  0.67058824]]

  [[ 0.58039216  0.37647059  0.74901961]
   [ 0.52941176  0.64313725  0.20784314]
   [ 0.94901961  0.85098039  0.35294118]
   ..., 
   [ 0.9254902   0.74509804  0.59607843]
   [ 0.10196078  0.85882353  0.50588235]
   [ 0.00392157  0.7254902   0.47843137]]]


 [[[ 0.19215686  0.0745098   0.11764706]
   [ 0.74509804  0.09019608  0.73333333]
   [ 0.96862745  0.87058824  0.86666667]
   ..., 
   [ 0.51372549  0.04313725  0.41960784]
   [ 0.08627451  0.01568627  0.96078431]
   [ 0.06666667  0.32941176  0.27843137]]

  [[ 0.36078431  0.60392157  0.29411765]
   [ 0.74117647  1.          0.34509804]
   [ 0.89803922  0.0745098   0.69411765]
   ..., 
   [ 0.87843137  0.44313725  0.80392157]
   [ 0.41568627  0.48627451  0.33333333]
   [ 0.17647059  0.20392157  0.31372549]]

  [[ 0.29019608  0.75686275  0.03921569]
   [ 0.83921569  0.57254902  0.14509804]
   [ 0.50588235  0.04705882  0.66666667]
   ..., 
   [ 0.85490196  0.52156863  0.30196078]
   [ 0.42745098  0.74117647  0.20392157]
   [ 0.1254902   0.8         0.43529412]]

  ..., 
  [[ 0.56470588  0.50588235  0.77647059]
   [ 0.94117647  0.65098039  0.94901961]
   [ 0.2627451   0.01176471  0.90980392]
   ..., 
   [ 0.43137255  0.88627451  0.25882353]
   [ 0.05490196  0.29803922  0.6745098 ]
   [ 0.18823529  0.22745098  0.96862745]]

  [[ 0.11764706  0.61568627  0.50588235]
   [ 0.92941176  0.9254902   0.23137255]
   [ 0.14117647  0.85882353  0.94901961]
   ..., 
   [ 0.97254902  0.65882353  0.95686275]
   [ 0.40784314  0.36470588  0.58823529]
   [ 0.79607843  0.31764706  0.85490196]]

  [[ 0.06666667  0.92156863  0.54901961]
   [ 0.5254902   0.66666667  0.77254902]
   [ 0.65490196  0.49019608  0.72156863]
   ..., 
   [ 0.43921569  0.73333333  0.10588235]
   [ 0.21568627  0.56078431  0.90588235]
   [ 0.4         0.3254902   0.79607843]]]


 ..., 
 [[[ 0.35686275  0.10980392  0.94117647]
   [ 0.21568627  0.29803922  0.74901961]
   [ 0.02745098  0.58431373  0.94117647]
   ..., 
   [ 0.94117647  0.95686275  0.61176471]
   [ 0.10588235  0.58039216  0.25098039]
   [ 0.58431373  0.41176471  0.35294118]]

  [[ 0.58823529  0.98039216  0.26666667]
   [ 0.67058824  0.72156863  0.74509804]
   [ 0.30196078  0.43529412  0.80784314]
   ..., 
   [ 0.77254902  0.62352941  0.53333333]
   [ 0.27058824  0.03921569  0.6627451 ]
   [ 0.80392157  0.86666667  0.30588235]]

  [[ 0.69803922  0.19215686  0.4627451 ]
   [ 0.91764706  0.18431373  0.61960784]
   [ 0.0745098   0.03137255  0.4627451 ]
   ..., 
   [ 0.16078431  0.4745098   0.69411765]
   [ 0.23921569  0.43921569  0.52941176]
   [ 0.98431373  0.96078431  0.90588235]]

  ..., 
  [[ 0.01568627  0.90588235  0.16470588]
   [ 0.14509804  0.78823529  0.63137255]
   [ 0.98823529  0.89411765  0.05098039]
   ..., 
   [ 0.64313725  0.96470588  0.29019608]
   [ 0.1254902   0.9254902   0.21568627]
   [ 0.83529412  0.57254902  0.01176471]]

  [[ 0.95294118  0.17647059  1.        ]
   [ 0.68627451  0.91372549  0.2745098 ]
   [ 0.4745098   0.82352941  0.94509804]
   ..., 
   [ 1.          0.58823529  0.8627451 ]
   [ 0.68627451  0.08235294  0.21568627]
   [ 0.74509804  0.66666667  0.04705882]]

  [[ 0.01176471  0.40784314  0.42352941]
   [ 0.14901961  0.81568627  0.48627451]
   [ 0.99607843  0.25882353  0.76078431]
   ..., 
   [ 0.17254902  0.68235294  0.2745098 ]
   [ 0.09411765  0.37647059  0.57647059]
   [ 0.34509804  0.50588235  0.34117647]]]


 [[[ 0.50588235  0.6         0.44313725]
   [ 0.52156863  0.25490196  0.85098039]
   [ 0.54901961  0.7254902   0.23137255]
   ..., 
   [ 0.28235294  0.5254902   0.47058824]
   [ 0.29019608  0.36078431  0.29411765]
   [ 0.21960784  0.51764706  0.9254902 ]]

  [[ 0.26666667  0.08627451  0.63529412]
   [ 0.82352941  0.62352941  0.42745098]
   [ 0.60392157  0.79215686  0.75294118]
   ..., 
   [ 0.13333333  0.20392157  0.36862745]
   [ 0.16862745  0.90196078  1.        ]
   [ 0.76078431  0.71372549  0.44313725]]

  [[ 0.74117647  0.87058824  0.24705882]
   [ 0.03921569  0.87058824  0.92941176]
   [ 0.44705882  0.05098039  0.4       ]
   ..., 
   [ 0.96078431  0.66666667  0.41176471]
   [ 0.11764706  0.58823529  0.69411765]
   [ 0.89411765  0.34901961  0.12941176]]

  ..., 
  [[ 0.02352941  0.5254902   0.39215686]
   [ 0.71764706  0.24313725  0.54901961]
   [ 0.9254902   0.78431373  0.63921569]
   ..., 
   [ 0.01960784  1.          0.56470588]
   [ 0.91764706  0.57647059  0.83137255]
   [ 0.70588235  0.88235294  0.37647059]]

  [[ 0.26666667  0.63921569  0.76078431]
   [ 0.45098039  0.65882353  0.99215686]
   [ 0.01176471  0.47058824  0.19215686]
   ..., 
   [ 0.18039216  0.11764706  0.30588235]
   [ 0.34509804  0.18039216  0.34509804]
   [ 0.64705882  0.7372549   0.88235294]]

  [[ 0.00392157  0.5254902   0.14509804]
   [ 0.95686275  0.16470588  0.5254902 ]
   [ 0.0627451   0.55294118  0.6       ]
   ..., 
   [ 0.20392157  0.06666667  0.21960784]
   [ 0.81960784  0.03921569  0.84705882]
   [ 0.23137255  0.29803922  0.03921569]]]


 [[[ 0.38431373  0.71372549  0.77647059]
   [ 0.0627451   0.22352941  0.43921569]
   [ 0.30196078  0.34901961  0.9372549 ]
   ..., 
   [ 0.23921569  0.39215686  0.61176471]
   [ 0.23529412  0.34509804  0.83921569]
   [ 0.92941176  0.19215686  0.66666667]]

  [[ 0.23137255  0.20392157  0.80784314]
   [ 0.47058824  0.81176471  0.76862745]
   [ 0.7372549   0.41568627  0.3372549 ]
   ..., 
   [ 0.64705882  0.89803922  0.65098039]
   [ 0.90588235  0.45098039  0.54901961]
   [ 0.65490196  0.56862745  0.91764706]]

  [[ 0.05490196  0.46666667  0.9254902 ]
   [ 0.66666667  0.26666667  0.38823529]
   [ 0.00392157  0.70196078  0.58431373]
   ..., 
   [ 0.16078431  0.69019608  0.96862745]
   [ 0.92156863  0.58039216  0.57647059]
   [ 0.3372549   0.25882353  0.04313725]]

  ..., 
  [[ 0.00392157  0.8627451   0.13333333]
   [ 0.02745098  0.13333333  0.76470588]
   [ 0.62745098  0.07058824  0.8745098 ]
   ..., 
   [ 0.78823529  0.99215686  0.20784314]
   [ 0.86666667  0.52156863  0.34901961]
   [ 0.00784314  0.65882353  0.70588235]]

  [[ 0.14117647  0.01176471  0.00784314]
   [ 0.41176471  0.60784314  0.48235294]
   [ 0.23137255  0.5254902   0.25098039]
   ..., 
   [ 0.12941176  0.82352941  0.39607843]
   [ 0.35294118  0.39215686  0.50588235]
   [ 0.75294118  0.54901961  0.43137255]]

  [[ 0.25882353  0.38431373  0.47843137]
   [ 0.61176471  0.60784314  0.11764706]
   [ 0.52941176  0.16470588  0.07843137]
   ..., 
   [ 0.40392157  0.42745098  0.17254902]
   [ 0.69803922  0.18823529  0.0627451 ]
   [ 0.62352941  0.37254902  0.49411765]]]]
Tests Passed

In [5]:

    

n_labels = 10
encoding_map = np.eye(n_labels)
def one_hot_encode(x):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
    """
    # TODO: Implement Function
    return np.array([encoding_map[label] for label in x])


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)


    

    




Tests Passed

In [6]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)


    

    




[[[[ 0.23137255  0.24313725  0.24705882]
   [ 0.16862745  0.18039216  0.17647059]
   [ 0.19607843  0.18823529  0.16862745]
   ..., 
   [ 0.61960784  0.51764706  0.42352941]
   [ 0.59607843  0.49019608  0.4       ]
   [ 0.58039216  0.48627451  0.40392157]]

  [[ 0.0627451   0.07843137  0.07843137]
   [ 0.          0.          0.        ]
   [ 0.07058824  0.03137255  0.        ]
   ..., 
   [ 0.48235294  0.34509804  0.21568627]
   [ 0.46666667  0.3254902   0.19607843]
   [ 0.47843137  0.34117647  0.22352941]]

  [[ 0.09803922  0.09411765  0.08235294]
   [ 0.0627451   0.02745098  0.        ]
   [ 0.19215686  0.10588235  0.03137255]
   ..., 
   [ 0.4627451   0.32941176  0.19607843]
   [ 0.47058824  0.32941176  0.19607843]
   [ 0.42745098  0.28627451  0.16470588]]

  ..., 
  [[ 0.81568627  0.66666667  0.37647059]
   [ 0.78823529  0.6         0.13333333]
   [ 0.77647059  0.63137255  0.10196078]
   ..., 
   [ 0.62745098  0.52156863  0.2745098 ]
   [ 0.21960784  0.12156863  0.02745098]
   [ 0.20784314  0.13333333  0.07843137]]

  [[ 0.70588235  0.54509804  0.37647059]
   [ 0.67843137  0.48235294  0.16470588]
   [ 0.72941176  0.56470588  0.11764706]
   ..., 
   [ 0.72156863  0.58039216  0.36862745]
   [ 0.38039216  0.24313725  0.13333333]
   [ 0.3254902   0.20784314  0.13333333]]

  [[ 0.69411765  0.56470588  0.45490196]
   [ 0.65882353  0.50588235  0.36862745]
   [ 0.70196078  0.55686275  0.34117647]
   ..., 
   [ 0.84705882  0.72156863  0.54901961]
   [ 0.59215686  0.4627451   0.32941176]
   [ 0.48235294  0.36078431  0.28235294]]]


 [[[ 0.60392157  0.69411765  0.73333333]
   [ 0.49411765  0.5372549   0.53333333]
   [ 0.41176471  0.40784314  0.37254902]
   ..., 
   [ 0.35686275  0.37254902  0.27843137]
   [ 0.34117647  0.35294118  0.27843137]
   [ 0.30980392  0.31764706  0.2745098 ]]

  [[ 0.54901961  0.62745098  0.6627451 ]
   [ 0.56862745  0.6         0.60392157]
   [ 0.49019608  0.49019608  0.4627451 ]
   ..., 
   [ 0.37647059  0.38823529  0.30588235]
   [ 0.30196078  0.31372549  0.24313725]
   [ 0.27843137  0.28627451  0.23921569]]

  [[ 0.54901961  0.60784314  0.64313725]
   [ 0.54509804  0.57254902  0.58431373]
   [ 0.45098039  0.45098039  0.43921569]
   ..., 
   [ 0.30980392  0.32156863  0.25098039]
   [ 0.26666667  0.2745098   0.21568627]
   [ 0.2627451   0.27058824  0.21568627]]

  ..., 
  [[ 0.68627451  0.65490196  0.65098039]
   [ 0.61176471  0.60392157  0.62745098]
   [ 0.60392157  0.62745098  0.66666667]
   ..., 
   [ 0.16470588  0.13333333  0.14117647]
   [ 0.23921569  0.20784314  0.22352941]
   [ 0.36470588  0.3254902   0.35686275]]

  [[ 0.64705882  0.60392157  0.50196078]
   [ 0.61176471  0.59607843  0.50980392]
   [ 0.62352941  0.63137255  0.55686275]
   ..., 
   [ 0.40392157  0.36470588  0.37647059]
   [ 0.48235294  0.44705882  0.47058824]
   [ 0.51372549  0.4745098   0.51372549]]

  [[ 0.63921569  0.58039216  0.47058824]
   [ 0.61960784  0.58039216  0.47843137]
   [ 0.63921569  0.61176471  0.52156863]
   ..., 
   [ 0.56078431  0.52156863  0.54509804]
   [ 0.56078431  0.5254902   0.55686275]
   [ 0.56078431  0.52156863  0.56470588]]]


 [[[ 1.          1.          1.        ]
   [ 0.99215686  0.99215686  0.99215686]
   [ 0.99215686  0.99215686  0.99215686]
   ..., 
   [ 0.99215686  0.99215686  0.99215686]
   [ 0.99215686  0.99215686  0.99215686]
   [ 0.99215686  0.99215686  0.99215686]]

  [[ 1.          1.          1.        ]
   [ 1.          1.          1.        ]
   [ 1.          1.          1.        ]
   ..., 
   [ 1.          1.          1.        ]
   [ 1.          1.          1.        ]
   [ 1.          1.          1.        ]]

  [[ 1.          1.          1.        ]
   [ 0.99607843  0.99607843  0.99607843]
   [ 0.99607843  0.99607843  0.99607843]
   ..., 
   [ 0.99607843  0.99607843  0.99607843]
   [ 0.99607843  0.99607843  0.99607843]
   [ 0.99607843  0.99607843  0.99607843]]

  ..., 
  [[ 0.44313725  0.47058824  0.43921569]
   [ 0.43529412  0.4627451   0.43529412]
   [ 0.41176471  0.43921569  0.41568627]
   ..., 
   [ 0.28235294  0.31764706  0.31372549]
   [ 0.28235294  0.31372549  0.30980392]
   [ 0.28235294  0.31372549  0.30980392]]

  [[ 0.43529412  0.4627451   0.43137255]
   [ 0.40784314  0.43529412  0.40784314]
   [ 0.38823529  0.41568627  0.38431373]
   ..., 
   [ 0.26666667  0.29411765  0.28627451]
   [ 0.2745098   0.29803922  0.29411765]
   [ 0.30588235  0.32941176  0.32156863]]

  [[ 0.41568627  0.44313725  0.41176471]
   [ 0.38823529  0.41568627  0.38431373]
   [ 0.37254902  0.4         0.36862745]
   ..., 
   [ 0.30588235  0.33333333  0.3254902 ]
   [ 0.30980392  0.33333333  0.3254902 ]
   [ 0.31372549  0.3372549   0.32941176]]]


 ..., 
 [[[ 0.27058824  0.27843137  0.20392157]
   [ 0.24313725  0.24313725  0.19215686]
   [ 0.22745098  0.22352941  0.18823529]
   ..., 
   [ 0.4627451   0.49019608  0.31372549]
   [ 0.4745098   0.49019608  0.28627451]
   [ 0.48235294  0.50588235  0.29019608]]

  [[ 0.2745098   0.27843137  0.19215686]
   [ 0.23137255  0.22745098  0.18431373]
   [ 0.2         0.19215686  0.16862745]
   ..., 
   [ 0.48235294  0.48235294  0.3254902 ]
   [ 0.45882353  0.47843137  0.29803922]
   [ 0.41568627  0.43921569  0.25490196]]

  [[ 0.28627451  0.28235294  0.18431373]
   [ 0.25490196  0.24705882  0.17647059]
   [ 0.20392157  0.19607843  0.17254902]
   ..., 
   [ 0.47058824  0.46666667  0.30980392]
   [ 0.45098039  0.45098039  0.27058824]
   [ 0.42745098  0.43529412  0.26666667]]

  ..., 
  [[ 0.76862745  0.82352941  0.37254902]
   [ 0.79215686  0.82745098  0.42745098]
   [ 0.76078431  0.78823529  0.41176471]
   ..., 
   [ 0.77647059  0.84313725  0.35294118]
   [ 0.81568627  0.85882353  0.42352941]
   [ 0.83137255  0.88235294  0.43529412]]

  [[ 0.74509804  0.80784314  0.36862745]
   [ 0.74901961  0.80784314  0.37254902]
   [ 0.74509804  0.80392157  0.36470588]
   ..., 
   [ 0.77647059  0.83921569  0.36862745]
   [ 0.76470588  0.81568627  0.37254902]
   [ 0.80784314  0.8627451   0.41960784]]

  [[ 0.71764706  0.76470588  0.36470588]
   [ 0.72156863  0.77254902  0.37647059]
   [ 0.71372549  0.75686275  0.36078431]
   ..., 
   [ 0.75686275  0.81176471  0.35686275]
   [ 0.74117647  0.8         0.34117647]
   [ 0.77647059  0.82745098  0.39215686]]]


 [[[ 0.61960784  0.61960784  0.61960784]
   [ 0.61960784  0.61960784  0.61568627]
   [ 0.61960784  0.62352941  0.60392157]
   ..., 
   [ 0.61176471  0.61568627  0.59607843]
   [ 0.61176471  0.61568627  0.59607843]
   [ 0.61176471  0.61176471  0.59215686]]

  [[ 0.61568627  0.61568627  0.61568627]
   [ 0.61960784  0.61960784  0.61568627]
   [ 0.61568627  0.61568627  0.60784314]
   ..., 
   [ 0.61176471  0.61568627  0.6       ]
   [ 0.61176471  0.61568627  0.59607843]
   [ 0.61176471  0.61176471  0.59607843]]

  [[ 0.61176471  0.61176471  0.61176471]
   [ 0.61568627  0.61568627  0.61176471]
   [ 0.61568627  0.61568627  0.60784314]
   ..., 
   [ 0.60784314  0.60784314  0.6       ]
   [ 0.60784314  0.60784314  0.6       ]
   [ 0.60784314  0.60784314  0.6       ]]

  ..., 
  [[ 0.22745098  0.26666667  0.26666667]
   [ 0.24313725  0.28235294  0.29019608]
   [ 0.21960784  0.25882353  0.2627451 ]
   ..., 
   [ 0.24313725  0.2745098   0.30196078]
   [ 0.18823529  0.22352941  0.23137255]
   [ 0.22352941  0.25882353  0.26666667]]

  [[ 0.20392157  0.24313725  0.23921569]
   [ 0.23529412  0.27058824  0.27843137]
   [ 0.16862745  0.20392157  0.20392157]
   ..., 
   [ 0.25882353  0.29803922  0.30980392]
   [ 0.21568627  0.25882353  0.2627451 ]
   [ 0.23529412  0.2745098   0.29019608]]

  [[ 0.19607843  0.22745098  0.23137255]
   [ 0.2         0.21960784  0.21960784]
   [ 0.20784314  0.23529412  0.23921569]
   ..., 
   [ 0.18039216  0.21176471  0.21176471]
   [ 0.22745098  0.25882353  0.26666667]
   [ 0.23921569  0.28235294  0.29019608]]]


 [[[ 0.76862745  0.67058824  0.51372549]
   [ 0.76862745  0.6745098   0.51372549]
   [ 0.75686275  0.65882353  0.50196078]
   ..., 
   [ 0.69411765  0.6         0.50980392]
   [ 0.67058824  0.57647059  0.48627451]
   [ 0.64705882  0.55294118  0.45882353]]

  [[ 0.77647059  0.67843137  0.52156863]
   [ 0.77647059  0.67843137  0.52156863]
   [ 0.76470588  0.6627451   0.50588235]
   ..., 
   [ 0.69803922  0.60784314  0.51764706]
   [ 0.6745098   0.58431373  0.49411765]
   [ 0.65490196  0.56470588  0.47058824]]

  [[ 0.76862745  0.67058824  0.51764706]
   [ 0.76862745  0.67058824  0.51764706]
   [ 0.75294118  0.65490196  0.50196078]
   ..., 
   [ 0.69019608  0.60392157  0.51764706]
   [ 0.66666667  0.58431373  0.49411765]
   [ 0.64705882  0.56078431  0.4745098 ]]

  ..., 
  [[ 0.7372549   0.61960784  0.40392157]
   [ 0.7372549   0.62745098  0.40392157]
   [ 0.74117647  0.62745098  0.4       ]
   ..., 
   [ 0.8         0.74509804  0.69803922]
   [ 0.78431373  0.74117647  0.70196078]
   [ 0.76862745  0.71764706  0.68235294]]

  [[ 0.74117647  0.62352941  0.40392157]
   [ 0.74117647  0.62745098  0.40784314]
   [ 0.74509804  0.63137255  0.4       ]
   ..., 
   [ 0.80392157  0.74509804  0.68627451]
   [ 0.78431373  0.73333333  0.69019608]
   [ 0.77254902  0.71764706  0.67843137]]

  [[ 0.72156863  0.60392157  0.39215686]
   [ 0.72156863  0.61176471  0.39215686]
   [ 0.72156863  0.60784314  0.38039216]
   ..., 
   [ 0.78039216  0.71372549  0.64705882]
   [ 0.76862745  0.70588235  0.65490196]
   [ 0.75686275  0.69411765  0.65098039]]]]
[[[[ 0.1372549   0.09803922  0.10196078]
   [ 0.10588235  0.08235294  0.08235294]
   [ 0.09803922  0.07843137  0.0745098 ]
   ..., 
   [ 0.51764706  0.50588235  0.50588235]
   [ 0.52156863  0.4745098   0.45490196]
   [ 0.49411765  0.45098039  0.44313725]]

  [[ 0.24705882  0.21568627  0.19607843]
   [ 0.1254902   0.10588235  0.08235294]
   [ 0.06666667  0.05098039  0.03137255]
   ..., 
   [ 0.4         0.37254902  0.34509804]
   [ 0.41176471  0.34901961  0.29803922]
   [ 0.39215686  0.3372549   0.30196078]]

  [[ 0.38823529  0.35686275  0.32941176]
   [ 0.19215686  0.17647059  0.14509804]
   [ 0.05882353  0.04705882  0.01960784]
   ..., 
   [ 0.18039216  0.16862745  0.15294118]
   [ 0.20392157  0.16078431  0.13333333]
   [ 0.20392157  0.17254902  0.16078431]]

  ..., 
  [[ 0.65098039  0.64705882  0.67058824]
   [ 0.64313725  0.63921569  0.65098039]
   [ 0.64313725  0.64313725  0.64705882]
   ..., 
   [ 0.67843137  0.6745098   0.66666667]
   [ 0.66666667  0.66666667  0.65882353]
   [ 0.65490196  0.65490196  0.65490196]]

  [[ 0.6627451   0.65882353  0.69019608]
   [ 0.6627451   0.65882353  0.67843137]
   [ 0.65882353  0.65882353  0.67058824]
   ..., 
   [ 0.6745098   0.67058824  0.66666667]
   [ 0.65882353  0.65490196  0.65490196]
   [ 0.64705882  0.64705882  0.65098039]]

  [[ 0.67843137  0.6745098   0.70196078]
   [ 0.68627451  0.68235294  0.69803922]
   [ 0.67843137  0.67843137  0.68627451]
   ..., 
   [ 0.66666667  0.65882353  0.6627451 ]
   [ 0.65882353  0.65490196  0.65882353]
   [ 0.65098039  0.65098039  0.65882353]]]


 [[[ 0.07843137  0.05882353  0.04705882]
   [ 0.07843137  0.05882353  0.04705882]
   [ 0.07058824  0.05098039  0.03921569]
   ..., 
   [ 0.07843137  0.0627451   0.0627451 ]
   [ 0.08235294  0.0627451   0.05490196]
   [ 0.08235294  0.0627451   0.05098039]]

  [[ 0.07843137  0.05882353  0.04705882]
   [ 0.07843137  0.05882353  0.04705882]
   [ 0.07058824  0.05098039  0.03921569]
   ..., 
   [ 0.07843137  0.0627451   0.05882353]
   [ 0.08235294  0.0627451   0.05098039]
   [ 0.08235294  0.0627451   0.05098039]]

  [[ 0.07843137  0.05882353  0.04705882]
   [ 0.07843137  0.05882353  0.04705882]
   [ 0.07058824  0.05098039  0.03921569]
   ..., 
   [ 0.07843137  0.0627451   0.05490196]
   [ 0.08235294  0.0627451   0.05098039]
   [ 0.08235294  0.0627451   0.05098039]]

  ..., 
  [[ 0.25882353  0.21176471  0.16078431]
   [ 0.31372549  0.2627451   0.20784314]
   [ 0.18431373  0.1372549   0.0745098 ]
   ..., 
   [ 0.5254902   0.5254902   0.39215686]
   [ 0.43137255  0.44313725  0.30196078]
   [ 0.38431373  0.4         0.25882353]]

  [[ 0.23529412  0.18823529  0.12941176]
   [ 0.21568627  0.16862745  0.10588235]
   [ 0.19607843  0.14901961  0.08627451]
   ..., 
   [ 0.48235294  0.49019608  0.3254902 ]
   [ 0.30980392  0.31764706  0.16470588]
   [ 0.28235294  0.29019608  0.14901961]]

  [[ 0.25098039  0.21176471  0.14901961]
   [ 0.21568627  0.17647059  0.11372549]
   [ 0.18823529  0.14901961  0.08235294]
   ..., 
   [ 0.60784314  0.61568627  0.43529412]
   [ 0.53333333  0.5372549   0.38039216]
   [ 0.34509804  0.34901961  0.2       ]]]


 [[[ 0.45490196  0.40392157  0.21960784]
   [ 0.45098039  0.41176471  0.23137255]
   [ 0.60784314  0.50196078  0.32156863]
   ..., 
   [ 0.68627451  0.51764706  0.30196078]
   [ 0.6627451   0.52156863  0.28235294]
   [ 0.55686275  0.46666667  0.20784314]]

  [[ 0.45490196  0.4         0.22745098]
   [ 0.47843137  0.42352941  0.25490196]
   [ 0.6         0.4745098   0.30980392]
   ..., 
   [ 0.58823529  0.43529412  0.22352941]
   [ 0.56862745  0.4745098   0.23529412]
   [ 0.52156863  0.48235294  0.21176471]]

  [[ 0.37254902  0.3372549   0.16078431]
   [ 0.38431373  0.32941176  0.17254902]
   [ 0.55294118  0.41568627  0.2627451 ]
   ..., 
   [ 0.56862745  0.43921569  0.22745098]
   [ 0.49411765  0.43529412  0.2       ]
   [ 0.49803922  0.49019608  0.24313725]]

  ..., 
  [[ 0.30196078  0.24705882  0.11372549]
   [ 0.34509804  0.28235294  0.14509804]
   [ 0.2745098   0.23137255  0.10588235]
   ..., 
   [ 0.18823529  0.15294118  0.07843137]
   [ 0.45490196  0.42352941  0.32941176]
   [ 0.62352941  0.55686275  0.47843137]]

  [[ 0.21568627  0.14509804  0.0627451 ]
   [ 0.25490196  0.18039216  0.09411765]
   [ 0.26666667  0.20784314  0.11764706]
   ..., 
   [ 0.16470588  0.11764706  0.05098039]
   [ 0.49411765  0.44705882  0.35294118]
   [ 0.62745098  0.57647059  0.49019608]]

  [[ 0.30588235  0.22352941  0.14509804]
   [ 0.28235294  0.19607843  0.11764706]
   [ 0.2627451   0.19607843  0.1254902 ]
   ..., 
   [ 0.20392157  0.14509804  0.07058824]
   [ 0.48627451  0.43137255  0.32941176]
   [ 0.60784314  0.56470588  0.48627451]]]


 ..., 
 [[[ 0.41568627  0.3372549   0.61176471]
   [ 0.48235294  0.4         0.50588235]
   [ 0.43529412  0.33333333  0.49019608]
   ..., 
   [ 0.51372549  0.4627451   0.61568627]
   [ 0.42745098  0.39215686  0.55686275]
   [ 0.55294118  0.52941176  0.61960784]]

  [[ 0.60784314  0.5254902   0.63529412]
   [ 0.6745098   0.57254902  0.44313725]
   [ 0.51372549  0.43529412  0.48627451]
   ..., 
   [ 0.51764706  0.46666667  0.60784314]
   [ 0.39215686  0.34901961  0.50980392]
   [ 0.56862745  0.5372549   0.63529412]]

  [[ 0.63529412  0.5254902   0.68235294]
   [ 0.62745098  0.49803922  0.43137255]
   [ 0.44313725  0.34509804  0.5254902 ]
   ..., 
   [ 0.73333333  0.71372549  0.65490196]
   [ 0.69411765  0.6745098   0.65882353]
   [ 0.78823529  0.77254902  0.71764706]]

  ..., 
  [[ 0.35686275  0.03921569  0.5254902 ]
   [ 0.34509804  0.03529412  0.49803922]
   [ 0.34509804  0.05098039  0.49803922]
   ..., 
   [ 0.40784314  0.24705882  0.49411765]
   [ 0.43529412  0.28235294  0.52156863]
   [ 0.43921569  0.25490196  0.53333333]]

  [[ 0.29803922  0.02352941  0.50196078]
   [ 0.30588235  0.01568627  0.47058824]
   [ 0.35686275  0.08235294  0.51764706]
   ..., 
   [ 0.42745098  0.27058824  0.49803922]
   [ 0.41960784  0.26666667  0.50980392]
   [ 0.44705882  0.24705882  0.54509804]]

  [[ 0.2627451   0.03137255  0.50196078]
   [ 0.26666667  0.02352941  0.47058824]
   [ 0.30980392  0.07058824  0.49803922]
   ..., 
   [ 0.45882353  0.28627451  0.5254902 ]
   [ 0.43137255  0.25098039  0.52156863]
   [ 0.38823529  0.16470588  0.49019608]]]


 [[[ 0.96862745  0.96470588  0.97254902]
   [ 0.96078431  0.97647059  0.98823529]
   [ 0.95686275  0.97254902  0.97647059]
   ..., 
   [ 0.45882353  0.38039216  0.32941176]
   [ 0.49803922  0.41960784  0.37647059]
   [ 0.61960784  0.56470588  0.54117647]]

  [[ 0.95294118  0.95686275  0.96862745]
   [ 0.95294118  0.96862745  0.97647059]
   [ 0.95294118  0.96078431  0.95686275]
   ..., 
   [ 0.44313725  0.35686275  0.29803922]
   [ 0.47843137  0.4         0.34509804]
   [ 0.63137255  0.56862745  0.52941176]]

  [[ 0.95686275  0.96078431  0.97647059]
   [ 0.96078431  0.97647059  0.98039216]
   [ 0.96862745  0.96862745  0.95686275]
   ..., 
   [ 0.52941176  0.42745098  0.36470588]
   [ 0.4745098   0.37647059  0.31764706]
   [ 0.50588235  0.43137255  0.38823529]]

  ..., 
  [[ 0.71372549  0.6627451   0.63529412]
   [ 0.77647059  0.7254902   0.69803922]
   [ 0.85490196  0.80392157  0.77647059]
   ..., 
   [ 0.61568627  0.54901961  0.44313725]
   [ 0.36862745  0.30588235  0.21960784]
   [ 0.50980392  0.45882353  0.41176471]]

  [[ 0.54509804  0.4627451   0.41960784]
   [ 0.49411765  0.40392157  0.35686275]
   [ 0.5254902   0.43529412  0.38431373]
   ..., 
   [ 0.61960784  0.55686275  0.43921569]
   [ 0.46666667  0.40784314  0.31372549]
   [ 0.45490196  0.40784314  0.35294118]]

  [[ 0.76862745  0.70196078  0.62745098]
   [ 0.7254902   0.63921569  0.54509804]
   [ 0.69019608  0.58823529  0.48235294]
   ..., 
   [ 0.59607843  0.54901961  0.42352941]
   [ 0.69411765  0.64313725  0.5372549 ]
   [ 0.63529412  0.59607843  0.52156863]]]


 [[[ 0.74117647  0.89803922  0.94117647]
   [ 0.76470588  0.90980392  0.94901961]
   [ 0.79607843  0.93333333  0.96470588]
   ..., 
   [ 0.65882353  0.75686275  0.81176471]
   [ 0.65882353  0.74901961  0.79215686]
   [ 0.65882353  0.7372549   0.78039216]]

  [[ 0.79607843  0.9372549   0.96470588]
   [ 0.83137255  0.96470588  0.98431373]
   [ 0.82352941  0.94901961  0.96862745]
   ..., 
   [ 0.58431373  0.67843137  0.76470588]
   [ 0.59215686  0.6745098   0.76470588]
   [ 0.60784314  0.67843137  0.77254902]]

  [[ 0.83529412  0.96862745  0.98431373]
   [ 0.81568627  0.94509804  0.96078431]
   [ 0.82745098  0.94509804  0.95686275]
   ..., 
   [ 0.58431373  0.6745098   0.76862745]
   [ 0.57647059  0.65490196  0.76470588]
   [ 0.56470588  0.63137255  0.75686275]]

  ..., 
  [[ 0.29019608  0.31764706  0.35294118]
   [ 0.21176471  0.32156863  0.47843137]
   [ 0.15294118  0.40784314  0.62352941]
   ..., 
   [ 0.18039216  0.15294118  0.18039216]
   [ 0.19607843  0.16862745  0.2       ]
   [ 0.27058824  0.24705882  0.25098039]]

  [[ 0.25490196  0.2627451   0.28627451]
   [ 0.16470588  0.22745098  0.35686275]
   [ 0.17647059  0.36078431  0.50980392]
   ..., 
   [ 0.14509804  0.08627451  0.17647059]
   [ 0.17254902  0.10588235  0.18431373]
   [ 0.22352941  0.17254902  0.2       ]]

  [[ 0.20784314  0.22745098  0.24705882]
   [ 0.15294118  0.2         0.25490196]
   [ 0.24705882  0.30588235  0.36862745]
   ..., 
   [ 0.18039216  0.10196078  0.19215686]
   [ 0.23137255  0.14901961  0.18431373]
   [ 0.29411765  0.23921569  0.2       ]]]]
[[[[ 0.10196078  0.09019608  0.1254902 ]
   [ 0.06666667  0.05490196  0.09803922]
   [ 0.05098039  0.03529412  0.09411765]
   ..., 
   [ 0.05882353  0.05490196  0.10980392]
   [ 0.09411765  0.09411765  0.14509804]
   [ 0.08627451  0.08235294  0.13333333]]

  [[ 0.07843137  0.06666667  0.10196078]
   [ 0.05098039  0.03921569  0.08627451]
   [ 0.05098039  0.03529412  0.09411765]
   ..., 
   [ 0.0745098   0.06666667  0.1372549 ]
   [ 0.08235294  0.07843137  0.1372549 ]
   [ 0.11372549  0.11372549  0.15294118]]

  [[ 0.05490196  0.04313725  0.07843137]
   [ 0.05098039  0.03921569  0.08235294]
   [ 0.05098039  0.03529412  0.09019608]
   ..., 
   [ 0.06666667  0.0627451   0.1254902 ]
   [ 0.09803922  0.09411765  0.14901961]
   [ 0.12156863  0.12156863  0.16470588]]

  ..., 
  [[ 0.35294118  0.42745098  0.5372549 ]
   [ 0.13333333  0.25098039  0.37254902]
   [ 0.10980392  0.21176471  0.35294118]
   ..., 
   [ 0.09019608  0.07843137  0.14509804]
   [ 0.0627451   0.05098039  0.11764706]
   [ 0.03529412  0.02352941  0.09019608]]

  [[ 0.30980392  0.41176471  0.55294118]
   [ 0.22745098  0.37647059  0.54509804]
   [ 0.1254902   0.26666667  0.43137255]
   ..., 
   [ 0.05490196  0.04313725  0.10980392]
   [ 0.0627451   0.05098039  0.11764706]
   [ 0.03921569  0.02745098  0.09411765]]

  [[ 0.50196078  0.61568627  0.76862745]
   [ 0.22745098  0.36470588  0.58431373]
   [ 0.09803922  0.23529412  0.41568627]
   ..., 
   [ 0.05098039  0.03921569  0.10588235]
   [ 0.04705882  0.03529412  0.10196078]
   [ 0.05098039  0.03921569  0.10588235]]]


 [[[ 0.36862745  0.3372549   0.22745098]
   [ 0.39607843  0.35686275  0.23921569]
   [ 0.37254902  0.33333333  0.21176471]
   ..., 
   [ 0.56862745  0.54117647  0.41568627]
   [ 0.56862745  0.54901961  0.42352941]
   [ 0.4745098   0.45882353  0.35294118]]

  [[ 0.34901961  0.32941176  0.21568627]
   [ 0.38039216  0.34901961  0.23137255]
   [ 0.39607843  0.35686275  0.23529412]
   ..., 
   [ 0.57254902  0.5372549   0.41568627]
   [ 0.57254902  0.54509804  0.41960784]
   [ 0.47843137  0.45882353  0.35294118]]

  [[ 0.3372549   0.32941176  0.21176471]
   [ 0.36862745  0.34509804  0.22352941]
   [ 0.41960784  0.38431373  0.2627451 ]
   ..., 
   [ 0.57254902  0.53333333  0.41568627]
   [ 0.57647059  0.54117647  0.41960784]
   [ 0.48235294  0.45490196  0.35294118]]

  ..., 
  [[ 0.80392157  0.80392157  0.8       ]
   [ 0.81568627  0.81568627  0.81176471]
   [ 0.78823529  0.78823529  0.78431373]
   ..., 
   [ 0.56862745  0.58431373  0.58431373]
   [ 0.58431373  0.59607843  0.61568627]
   [ 0.49019608  0.50196078  0.52941176]]

  [[ 0.78823529  0.78823529  0.78823529]
   [ 0.80392157  0.80392157  0.80392157]
   [ 0.77647059  0.77647059  0.77647059]
   ..., 
   [ 0.60392157  0.61960784  0.6627451 ]
   [ 0.61960784  0.63529412  0.69411765]
   [ 0.5254902   0.54117647  0.6       ]]

  [[ 0.74509804  0.74509804  0.74117647]
   [ 0.7372549   0.7372549   0.73333333]
   [ 0.68627451  0.68627451  0.67843137]
   ..., 
   [ 0.63529412  0.64313725  0.71372549]
   [ 0.63921569  0.65098039  0.72156863]
   [ 0.52941176  0.5372549   0.60784314]]]


 [[[ 0.71764706  0.72941176  0.69803922]
   [ 0.61960784  0.65490196  0.59607843]
   [ 0.65098039  0.6745098   0.62745098]
   ..., 
   [ 0.55686275  0.57647059  0.56470588]
   [ 0.5372549   0.57254902  0.55294118]
   [ 0.56470588  0.58823529  0.57254902]]

  [[ 0.49019608  0.5254902   0.47058824]
   [ 0.38039216  0.43529412  0.35294118]
   [ 0.39215686  0.43921569  0.37254902]
   ..., 
   [ 0.24705882  0.29019608  0.26666667]
   [ 0.23137255  0.29019608  0.2627451 ]
   [ 0.2627451   0.30980392  0.28235294]]

  [[ 0.41960784  0.4745098   0.40392157]
   [ 0.33333333  0.40784314  0.30980392]
   [ 0.34509804  0.40784314  0.3254902 ]
   ..., 
   [ 0.24705882  0.30980392  0.27843137]
   [ 0.20784314  0.28627451  0.25098039]
   [ 0.23529412  0.30196078  0.27058824]]

  ..., 
  [[ 0.42352941  0.45490196  0.40392157]
   [ 0.38431373  0.41960784  0.34117647]
   [ 0.46666667  0.50980392  0.41176471]
   ..., 
   [ 0.44705882  0.49019608  0.44313725]
   [ 0.40784314  0.45098039  0.40392157]
   [ 0.35686275  0.39607843  0.35294118]]

  [[ 0.78039216  0.80392157  0.77254902]
   [ 0.77254902  0.79607843  0.74509804]
   [ 0.81176471  0.83921569  0.76862745]
   ..., 
   [ 0.79607843  0.81960784  0.79215686]
   [ 0.78431373  0.80392157  0.78039216]
   [ 0.74117647  0.76078431  0.7372549 ]]

  [[ 0.98431373  1.          0.98823529]
   [ 0.97254902  0.98823529  0.96470588]
   [ 0.97647059  0.99215686  0.95686275]
   ..., 
   [ 0.98039216  0.98431373  0.98039216]
   [ 0.98039216  0.98039216  0.98039216]
   [ 0.98039216  0.98431373  0.98039216]]]


 ..., 
 [[[ 0.6745098   0.67843137  0.59607843]
   [ 0.67058824  0.6745098   0.59607843]
   [ 0.69803922  0.69803922  0.61960784]
   ..., 
   [ 0.41960784  0.41176471  0.30196078]
   [ 0.41568627  0.40392157  0.30980392]
   [ 0.4         0.38823529  0.30588235]]

  [[ 0.64705882  0.63921569  0.56078431]
   [ 0.62352941  0.61568627  0.54117647]
   [ 0.70588235  0.69803922  0.62352941]
   ..., 
   [ 0.45882353  0.43921569  0.3254902 ]
   [ 0.45882353  0.43921569  0.3372549 ]
   [ 0.43529412  0.41568627  0.3254902 ]]

  [[ 0.68235294  0.6627451   0.58823529]
   [ 0.61176471  0.59215686  0.51764706]
   [ 0.68235294  0.6627451   0.58823529]
   ..., 
   [ 0.47058824  0.44705882  0.3254902 ]
   [ 0.4745098   0.44705882  0.3372549 ]
   [ 0.46666667  0.43921569  0.34509804]]

  ..., 
  [[ 0.47843137  0.45882353  0.36862745]
   [ 0.47058824  0.45098039  0.36470588]
   [ 0.45490196  0.43921569  0.34901961]
   ..., 
   [ 0.48627451  0.49803922  0.39215686]
   [ 0.4745098   0.49411765  0.39215686]
   [ 0.45882353  0.47843137  0.38039216]]

  [[ 0.43529412  0.41176471  0.30588235]
   [ 0.43921569  0.41960784  0.3254902 ]
   [ 0.48235294  0.48627451  0.39607843]
   ..., 
   [ 0.47843137  0.45490196  0.32941176]
   [ 0.45882353  0.4627451   0.35294118]
   [ 0.44313725  0.45882353  0.35294118]]

  [[ 0.43921569  0.41176471  0.30196078]
   [ 0.45098039  0.42352941  0.32941176]
   [ 0.4627451   0.47058824  0.37647059]
   ..., 
   [ 0.48627451  0.45098039  0.31372549]
   [ 0.43137255  0.43137255  0.31764706]
   [ 0.4         0.41568627  0.30980392]]]


 [[[ 0.37254902  0.34509804  0.2       ]
   [ 0.36078431  0.34509804  0.19215686]
   [ 0.35686275  0.34117647  0.19607843]
   ..., 
   [ 0.20784314  0.20392157  0.11764706]
   [ 0.20392157  0.2         0.11764706]
   [ 0.21176471  0.21176471  0.1254902 ]]

  [[ 0.4         0.36862745  0.20784314]
   [ 0.41568627  0.38039216  0.2       ]
   [ 0.41960784  0.38431373  0.21176471]
   ..., 
   [ 0.20392157  0.20392157  0.1254902 ]
   [ 0.19215686  0.18823529  0.10588235]
   [ 0.23137255  0.22745098  0.1254902 ]]

  [[ 0.41960784  0.38431373  0.20392157]
   [ 0.42745098  0.38823529  0.20392157]
   [ 0.41176471  0.38039216  0.20392157]
   ..., 
   [ 0.20784314  0.20784314  0.1254902 ]
   [ 0.21568627  0.20784314  0.11764706]
   [ 0.24313725  0.23529412  0.1254902 ]]

  ..., 
  [[ 0.37647059  0.36078431  0.23529412]
   [ 0.24313725  0.23921569  0.15686275]
   [ 0.20784314  0.21176471  0.14117647]
   ..., 
   [ 0.19607843  0.19607843  0.12941176]
   [ 0.21568627  0.21568627  0.14117647]
   [ 0.23529412  0.22745098  0.15294118]]

  [[ 0.31372549  0.29803922  0.2       ]
   [ 0.23137255  0.22745098  0.15294118]
   [ 0.19607843  0.2         0.12941176]
   ..., 
   [ 0.21960784  0.20392157  0.13333333]
   [ 0.25098039  0.23529412  0.15294118]
   [ 0.25098039  0.23529412  0.14901961]]

  [[ 0.18431373  0.18039216  0.1254902 ]
   [ 0.15294118  0.15686275  0.10980392]
   [ 0.15294118  0.15686275  0.09803922]
   ..., 
   [ 0.30980392  0.29019608  0.17647059]
   [ 0.25098039  0.23137255  0.14901961]
   [ 0.23921569  0.22352941  0.14901961]]]


 [[[ 0.60784314  0.40392157  0.43137255]
   [ 0.59607843  0.39215686  0.41960784]
   [ 0.60784314  0.40392157  0.43137255]
   ..., 
   [ 0.23137255  0.14901961  0.17254902]
   [ 0.22352941  0.15686275  0.18039216]
   [ 0.22352941  0.16078431  0.19607843]]

  [[ 0.60784314  0.40784314  0.43529412]
   [ 0.58039216  0.38039216  0.40784314]
   [ 0.6         0.4         0.42745098]
   ..., 
   [ 0.28627451  0.18823529  0.20784314]
   [ 0.21960784  0.15294118  0.17647059]
   [ 0.21960784  0.16078431  0.19215686]]

  [[ 0.61176471  0.41176471  0.43921569]
   [ 0.58823529  0.38823529  0.41568627]
   [ 0.58039216  0.38039216  0.40784314]
   ..., 
   [ 0.41176471  0.30980392  0.3254902 ]
   [ 0.23137255  0.16470588  0.18823529]
   [ 0.2         0.14117647  0.17254902]]

  ..., 
  [[ 0.45490196  0.43921569  0.5254902 ]
   [ 0.44313725  0.43529412  0.50980392]
   [ 0.44313725  0.44313725  0.50196078]
   ..., 
   [ 0.42745098  0.38039216  0.44705882]
   [ 0.28235294  0.23921569  0.29803922]
   [ 0.42352941  0.37647059  0.43529412]]

  [[ 0.45490196  0.44313725  0.52156863]
   [ 0.44705882  0.43529412  0.50980392]
   [ 0.45098039  0.43921569  0.51372549]
   ..., 
   [ 0.43137255  0.41176471  0.48627451]
   [ 0.23137255  0.19607843  0.26666667]
   [ 0.29411765  0.23921569  0.30980392]]

  [[ 0.46666667  0.45490196  0.52941176]
   [ 0.45490196  0.44313725  0.51764706]
   [ 0.45490196  0.44313725  0.51764706]
   ..., 
   [ 0.47058824  0.45882353  0.5372549 ]
   [ 0.39607843  0.37254902  0.44705882]
   [ 0.24705882  0.19607843  0.26666667]]]]
[[[[ 0.69803922  0.69019608  0.74117647]
   [ 0.69803922  0.69019608  0.74117647]
   [ 0.69803922  0.69019608  0.74117647]
   ..., 
   [ 0.66666667  0.65882353  0.70588235]
   [ 0.65882353  0.65098039  0.69411765]
   [ 0.64705882  0.63921569  0.68235294]]

  [[ 0.70588235  0.69803922  0.74901961]
   [ 0.70196078  0.69411765  0.74509804]
   [ 0.70588235  0.69803922  0.74901961]
   ..., 
   [ 0.67843137  0.67058824  0.71372549]
   [ 0.67058824  0.6627451   0.70588235]
   [ 0.65882353  0.65098039  0.69411765]]

  [[ 0.69411765  0.68627451  0.7372549 ]
   [ 0.69411765  0.68627451  0.7372549 ]
   [ 0.69803922  0.69019608  0.74117647]
   ..., 
   [ 0.67058824  0.6627451   0.70588235]
   [ 0.6627451   0.65490196  0.69803922]
   [ 0.65490196  0.64705882  0.69019608]]

  ..., 
  [[ 0.43921569  0.41960784  0.41960784]
   [ 0.44313725  0.42745098  0.42352941]
   [ 0.44705882  0.43137255  0.43137255]
   ..., 
   [ 0.39215686  0.38039216  0.36862745]
   [ 0.38431373  0.36862745  0.36470588]
   [ 0.39607843  0.37254902  0.37254902]]

  [[ 0.43921569  0.4         0.39607843]
   [ 0.43921569  0.40392157  0.4       ]
   [ 0.44313725  0.40392157  0.40392157]
   ..., 
   [ 0.4         0.37254902  0.36470588]
   [ 0.4         0.36470588  0.35686275]
   [ 0.4         0.36078431  0.35686275]]

  [[ 0.40392157  0.37647059  0.36078431]
   [ 0.39215686  0.36470588  0.35294118]
   [ 0.40392157  0.37254902  0.36862745]
   ..., 
   [ 0.36078431  0.32941176  0.31372549]
   [ 0.36470588  0.3372549   0.31372549]
   [ 0.35686275  0.32941176  0.30196078]]]


 [[[ 0.11372549  0.16862745  0.03921569]
   [ 0.08627451  0.14117647  0.01568627]
   [ 0.09803922  0.14509804  0.0627451 ]
   ..., 
   [ 0.77254902  0.85882353  0.5372549 ]
   [ 0.77647059  0.85882353  0.5372549 ]
   [ 0.78039216  0.87058824  0.54901961]]

  [[ 0.12156863  0.18039216  0.03529412]
   [ 0.10588235  0.16078431  0.02352941]
   [ 0.06666667  0.11372549  0.02352941]
   ..., 
   [ 0.82352941  0.90980392  0.58039216]
   [ 0.81960784  0.90588235  0.58039216]
   [ 0.81960784  0.90588235  0.58039216]]

  [[ 0.15686275  0.21568627  0.0627451 ]
   [ 0.12156863  0.17647059  0.03137255]
   [ 0.07843137  0.12941176  0.02745098]
   ..., 
   [ 0.82352941  0.90980392  0.58823529]
   [ 0.82352941  0.90980392  0.58431373]
   [ 0.82352941  0.90980392  0.58431373]]

  ..., 
  [[ 0.17647059  0.14901961  0.09019608]
   [ 0.09411765  0.08235294  0.04313725]
   [ 0.0627451   0.05490196  0.02745098]
   ..., 
   [ 0.09803922  0.11372549  0.1254902 ]
   [ 0.09411765  0.10980392  0.12156863]
   [ 0.09411765  0.10980392  0.12156863]]

  [[ 0.08235294  0.07058824  0.02745098]
   [ 0.07058824  0.05098039  0.01176471]
   [ 0.10588235  0.0627451   0.01960784]
   ..., 
   [ 0.10196078  0.11764706  0.12941176]
   [ 0.11372549  0.12941176  0.14117647]
   [ 0.10980392  0.1254902   0.1372549 ]]

  [[ 0.20784314  0.15686275  0.09019608]
   [ 0.31764706  0.24313725  0.14901961]
   [ 0.38039216  0.2745098   0.16862745]
   ..., 
   [ 0.08627451  0.10196078  0.11372549]
   [ 0.09411765  0.10980392  0.12156863]
   [ 0.09019608  0.10588235  0.11764706]]]


 [[[ 0.14117647  0.25490196  0.4       ]
   [ 0.12941176  0.21568627  0.42352941]
   [ 0.08235294  0.18431373  0.4627451 ]
   ..., 
   [ 0.10196078  0.1254902   0.15686275]
   [ 0.10196078  0.12156863  0.12156863]
   [ 0.11372549  0.11372549  0.12156863]]

  [[ 0.21568627  0.41960784  0.47058824]
   [ 0.18431373  0.36862745  0.42352941]
   [ 0.05882353  0.24705882  0.44313725]
   ..., 
   [ 0.08627451  0.2         0.41176471]
   [ 0.09019608  0.19215686  0.39215686]
   [ 0.08235294  0.18039216  0.38039216]]

  [[ 0.32156863  0.45490196  0.44705882]
   [ 0.36862745  0.49803922  0.4       ]
   [ 0.30980392  0.45882353  0.42352941]
   ..., 
   [ 0.18431373  0.3254902   0.6       ]
   [ 0.18431373  0.3372549   0.61176471]
   [ 0.17647059  0.33333333  0.6       ]]

  ..., 
  [[ 0.62352941  0.62745098  0.58039216]
   [ 0.63529412  0.62352941  0.58431373]
   [ 0.65098039  0.62745098  0.59215686]
   ..., 
   [ 0.73333333  0.70588235  0.69411765]
   [ 0.7254902   0.69019608  0.68235294]
   [ 0.71764706  0.68235294  0.6745098 ]]

  [[ 0.65098039  0.6627451   0.62745098]
   [ 0.66666667  0.6627451   0.63137255]
   [ 0.67843137  0.6627451   0.63529412]
   ..., 
   [ 0.71764706  0.69019608  0.68235294]
   [ 0.70980392  0.67843137  0.67058824]
   [ 0.70588235  0.6745098   0.67058824]]

  [[ 0.65882353  0.68235294  0.65098039]
   [ 0.67058824  0.67843137  0.65490196]
   [ 0.68627451  0.67843137  0.65882353]
   ..., 
   [ 0.71372549  0.69019608  0.67843137]
   [ 0.70980392  0.6745098   0.66666667]
   [ 0.70588235  0.6745098   0.66666667]]]


 ..., 
 [[[ 0.27058824  0.34509804  0.44705882]
   [ 0.35294118  0.4745098   0.58823529]
   [ 0.35294118  0.49803922  0.61960784]
   ..., 
   [ 0.00784314  0.01176471  0.07058824]
   [ 0.00784314  0.00784314  0.0627451 ]
   [ 0.00784314  0.00784314  0.05882353]]

  [[ 0.11372549  0.15294118  0.25098039]
   [ 0.05882353  0.10588235  0.20784314]
   [ 0.05098039  0.09803922  0.2       ]
   ..., 
   [ 0.00392157  0.          0.00784314]
   [ 0.          0.          0.00392157]
   [ 0.          0.00392157  0.00784314]]

  [[ 0.01568627  0.01176471  0.01960784]
   [ 0.01568627  0.00392157  0.00784314]
   [ 0.01176471  0.00392157  0.00784314]
   ..., 
   [ 0.          0.          0.00392157]
   [ 0.          0.          0.00784314]
   [ 0.          0.          0.01176471]]

  ..., 
  [[ 0.00392157  0.00392157  0.01960784]
   [ 0.          0.          0.00392157]
   [ 0.          0.          0.        ]
   ..., 
   [ 0.03921569  0.03137255  0.04313725]
   [ 0.00784314  0.00784314  0.01568627]
   [ 0.00392157  0.00392157  0.01568627]]

  [[ 0.03137255  0.0627451   0.12941176]
   [ 0.00392157  0.01568627  0.05098039]
   [ 0.          0.00392157  0.01176471]
   ..., 
   [ 0.02745098  0.03921569  0.09019608]
   [ 0.01176471  0.01568627  0.03921569]
   [ 0.00392157  0.00392157  0.01176471]]

  [[ 0.14509804  0.25098039  0.42352941]
   [ 0.09411765  0.16862745  0.30196078]
   [ 0.03921569  0.0745098   0.15294118]
   ..., 
   [ 0.07843137  0.11764706  0.23921569]
   [ 0.05098039  0.07843137  0.16862745]
   [ 0.02352941  0.03921569  0.09019608]]]


 [[[ 0.7254902   0.79215686  0.81176471]
   [ 0.67843137  0.77647059  0.80392157]
   [ 0.69019608  0.81176471  0.85098039]
   ..., 
   [ 0.14509804  0.17647059  0.24313725]
   [ 0.10196078  0.1372549   0.2       ]
   [ 0.12941176  0.19607843  0.27058824]]

  [[ 0.42745098  0.47058824  0.46666667]
   [ 0.4627451   0.52941176  0.52941176]
   [ 0.4745098   0.56078431  0.56470588]
   ..., 
   [ 0.16862745  0.19607843  0.23529412]
   [ 0.12941176  0.13333333  0.16862745]
   [ 0.15686275  0.18823529  0.22745098]]

  [[ 0.20392157  0.22745098  0.21568627]
   [ 0.22745098  0.26666667  0.25098039]
   [ 0.22745098  0.28235294  0.2627451 ]
   ..., 
   [ 0.18039216  0.22745098  0.26666667]
   [ 0.15686275  0.18431373  0.22745098]
   [ 0.18431373  0.22745098  0.26666667]]

  ..., 
  [[ 0.63529412  0.62745098  0.6745098 ]
   [ 0.61960784  0.61176471  0.65490196]
   [ 0.62352941  0.61568627  0.65882353]
   ..., 
   [ 0.05882353  0.05098039  0.09019608]
   [ 0.05490196  0.04705882  0.09019608]
   [ 0.07058824  0.0627451   0.10588235]]

  [[ 0.61176471  0.60392157  0.65882353]
   [ 0.59607843  0.58823529  0.63921569]
   [ 0.59607843  0.58823529  0.63921569]
   ..., 
   [ 0.05098039  0.04313725  0.08235294]
   [ 0.05882353  0.05098039  0.09411765]
   [ 0.1254902   0.11764706  0.16078431]]

  [[ 0.58431373  0.57647059  0.63137255]
   [ 0.56470588  0.55686275  0.61176471]
   [ 0.57254902  0.56470588  0.61960784]
   ..., 
   [ 0.0627451   0.05490196  0.09411765]
   [ 0.20392157  0.19607843  0.23921569]
   [ 0.38431373  0.37647059  0.41960784]]]


 [[[ 0.76470588  0.71764706  0.67058824]
   [ 0.75686275  0.70980392  0.6627451 ]
   [ 0.76078431  0.71372549  0.66666667]
   ..., 
   [ 0.22352941  0.22352941  0.22352941]
   [ 0.20392157  0.20392157  0.20392157]
   [ 0.03137255  0.03137255  0.03137255]]

  [[ 0.77254902  0.72156863  0.67843137]
   [ 0.76470588  0.71764706  0.6745098 ]
   [ 0.77254902  0.7254902   0.68235294]
   ..., 
   [ 0.34117647  0.34117647  0.34117647]
   [ 0.31372549  0.31372549  0.31372549]
   [ 0.03921569  0.03921569  0.03921569]]

  [[ 0.77254902  0.72156863  0.68627451]
   [ 0.76862745  0.71764706  0.68235294]
   [ 0.77647059  0.7254902   0.69411765]
   ..., 
   [ 0.43137255  0.43137255  0.43137255]
   [ 0.41568627  0.41568627  0.41568627]
   [ 0.04705882  0.04705882  0.04705882]]

  ..., 
  [[ 0.78039216  0.7254902   0.69411765]
   [ 0.76862745  0.72156863  0.68627451]
   [ 0.78039216  0.74117647  0.69803922]
   ..., 
   [ 0.13333333  0.10980392  0.08235294]
   [ 0.10980392  0.09019608  0.07058824]
   [ 0.08627451  0.0745098   0.0627451 ]]

  [[ 0.77254902  0.7254902   0.69019608]
   [ 0.76470588  0.71764706  0.68235294]
   [ 0.77254902  0.72941176  0.69411765]
   ..., 
   [ 0.15294118  0.12156863  0.08235294]
   [ 0.14509804  0.12156863  0.08627451]
   [ 0.1372549   0.11372549  0.08627451]]

  [[ 0.75686275  0.71764706  0.68235294]
   [ 0.75686275  0.70588235  0.6745098 ]
   [ 0.76470588  0.70980392  0.67843137]
   ..., 
   [ 0.16470588  0.12941176  0.08235294]
   [ 0.16862745  0.12941176  0.09019608]
   [ 0.16078431  0.1254902   0.09411765]]]]
[[[[ 1.          1.          0.99607843]
   [ 0.98823529  0.98823529  0.98823529]
   [ 0.99215686  0.98823529  0.99607843]
   ..., 
   [ 0.64705882  0.69411765  0.72156863]
   [ 0.95294118  0.96470588  0.96862745]
   [ 0.99607843  0.99215686  0.98823529]]

  [[ 1.          1.          0.99607843]
   [ 0.98823529  0.98823529  0.98823529]
   [ 0.99607843  0.99607843  1.        ]
   ..., 
   [ 0.50980392  0.56470588  0.63137255]
   [ 0.88235294  0.90980392  0.9372549 ]
   [ 0.99215686  1.          1.        ]]

  [[ 1.          1.          1.        ]
   [ 0.99607843  0.99607843  0.99607843]
   [ 0.97254902  0.96862745  0.97647059]
   ..., 
   [ 0.55294118  0.60784314  0.68627451]
   [ 0.8627451   0.89019608  0.92156863]
   [ 0.99215686  1.          1.        ]]

  ..., 
  [[ 0.91372549  0.91764706  0.91764706]
   [ 0.84705882  0.84705882  0.84705882]
   [ 0.94509804  0.94509804  0.94509804]
   ..., 
   [ 0.03529412  0.04313725  0.04313725]
   [ 0.07058824  0.0745098   0.0745098 ]
   [ 0.6627451   0.67058824  0.66666667]]

  [[ 1.          1.          1.        ]
   [ 1.          1.          1.        ]
   [ 0.99215686  0.99215686  0.99215686]
   ..., 
   [ 0.08235294  0.09019608  0.08627451]
   [ 0.44313725  0.45098039  0.44705882]
   [ 0.92156863  0.92941176  0.9254902 ]]

  [[ 1.          1.          1.        ]
   [ 0.98431373  0.98431373  0.98431373]
   [ 0.99215686  0.99215686  0.99215686]
   ..., 
   [ 0.6745098   0.68235294  0.67843137]
   [ 0.90196078  0.90980392  0.90588235]
   [ 0.96862745  0.97254902  0.97254902]]]


 [[[ 0.49803922  0.56862745  0.65490196]
   [ 0.49411765  0.56470588  0.65098039]
   [ 0.49803922  0.56862745  0.65490196]
   ..., 
   [ 0.49019608  0.55686275  0.62352941]
   [ 0.49019608  0.55686275  0.62352941]
   [ 0.48627451  0.55294118  0.61960784]]

  [[ 0.49019608  0.56078431  0.64313725]
   [ 0.49019608  0.56078431  0.63921569]
   [ 0.49411765  0.56470588  0.64313725]
   ..., 
   [ 0.49019608  0.55686275  0.61568627]
   [ 0.48627451  0.55686275  0.61176471]
   [ 0.48627451  0.55294118  0.61176471]]

  [[ 0.49411765  0.56862745  0.63529412]
   [ 0.48627451  0.56078431  0.62745098]
   [ 0.49411765  0.56862745  0.63529412]
   ..., 
   [ 0.48627451  0.56078431  0.61176471]
   [ 0.48235294  0.55294118  0.60784314]
   [ 0.48235294  0.55294118  0.60784314]]

  ..., 
  [[ 0.32941176  0.40784314  0.4627451 ]
   [ 0.33333333  0.40784314  0.4627451 ]
   [ 0.34117647  0.41568627  0.47058824]
   ..., 
   [ 0.25490196  0.3254902   0.37254902]
   [ 0.30980392  0.38039216  0.42745098]
   [ 0.34509804  0.41568627  0.4627451 ]]

  [[ 0.3372549   0.41176471  0.47058824]
   [ 0.3254902   0.4         0.45490196]
   [ 0.3254902   0.4         0.45490196]
   ..., 
   [ 0.28627451  0.35686275  0.40392157]
   [ 0.3254902   0.39607843  0.44313725]
   [ 0.34117647  0.41176471  0.45882353]]

  [[ 0.33333333  0.40784314  0.4627451 ]
   [ 0.33333333  0.40392157  0.45882353]
   [ 0.3254902   0.4         0.45490196]
   ..., 
   [ 0.28235294  0.35294118  0.4       ]
   [ 0.30588235  0.37647059  0.42352941]
   [ 0.32156863  0.39215686  0.43921569]]]


 [[[ 0.45490196  0.27843137  0.10196078]
   [ 0.25098039  0.13333333  0.03921569]
   [ 0.0745098   0.02352941  0.00784314]
   ..., 
   [ 0.58039216  0.32941176  0.14901961]
   [ 0.6627451   0.37647059  0.18039216]
   [ 0.7372549   0.44705882  0.23137255]]

  [[ 0.44705882  0.26666667  0.08627451]
   [ 0.25098039  0.1372549   0.04313725]
   [ 0.07058824  0.02352941  0.00784314]
   ..., 
   [ 0.58431373  0.32941176  0.16862745]
   [ 0.63921569  0.36862745  0.17647059]
   [ 0.7254902   0.44705882  0.23137255]]

  [[ 0.44705882  0.25882353  0.09019608]
   [ 0.24313725  0.13333333  0.04313725]
   [ 0.06666667  0.02352941  0.00784314]
   ..., 
   [ 0.61568627  0.35294118  0.18431373]
   [ 0.68627451  0.4         0.2       ]
   [ 0.7254902   0.44705882  0.22745098]]

  ..., 
  [[ 0.94509804  0.94117647  0.91764706]
   [ 0.95294118  0.94901961  0.9254902 ]
   [ 0.94509804  0.94509804  0.9254902 ]
   ..., 
   [ 0.12156863  0.07058824  0.01568627]
   [ 0.1372549   0.07843137  0.01960784]
   [ 0.15294118  0.07843137  0.01960784]]

  [[ 0.84313725  0.82745098  0.78823529]
   [ 0.90196078  0.89019608  0.8627451 ]
   [ 0.92941176  0.92156863  0.90196078]
   ..., 
   [ 0.09019608  0.05098039  0.01176471]
   [ 0.09803922  0.05098039  0.00784314]
   [ 0.10196078  0.05098039  0.01176471]]

  [[ 0.47058824  0.42352941  0.37254902]
   [ 0.54117647  0.49803922  0.45098039]
   [ 0.60784314  0.57254902  0.5254902 ]
   ..., 
   [ 0.17254902  0.09803922  0.02745098]
   [ 0.16078431  0.08627451  0.02352941]
   [ 0.14901961  0.0745098   0.01960784]]]


 ..., 
 [[[ 0.23921569  0.28627451  0.29803922]
   [ 0.18039216  0.22745098  0.26666667]
   [ 0.15294118  0.19215686  0.25882353]
   ..., 
   [ 0.27843137  0.35294118  0.31372549]
   [ 0.24313725  0.30588235  0.29411765]
   [ 0.17647059  0.22745098  0.23921569]]

  [[ 0.24705882  0.29411765  0.30196078]
   [ 0.17647059  0.22745098  0.2627451 ]
   [ 0.1254902   0.16862745  0.22745098]
   ..., 
   [ 0.28627451  0.34901961  0.32156863]
   [ 0.27843137  0.32941176  0.31372549]
   [ 0.19607843  0.23529412  0.24313725]]

  [[ 0.24705882  0.31372549  0.30196078]
   [ 0.22352941  0.29411765  0.29411765]
   [ 0.24705882  0.30980392  0.31764706]
   ..., 
   [ 0.32156863  0.37647059  0.35686275]
   [ 0.29803922  0.34509804  0.32156863]
   [ 0.20784314  0.24705882  0.24313725]]

  ..., 
  [[ 0.51372549  0.52156863  0.4       ]
   [ 0.61176471  0.60392157  0.4627451 ]
   [ 0.63137255  0.61568627  0.4745098 ]
   ..., 
   [ 0.91764706  0.8745098   0.67843137]
   [ 0.8627451   0.8         0.61176471]
   [ 0.70980392  0.65098039  0.49411765]]

  [[ 0.41176471  0.42352941  0.3254902 ]
   [ 0.41960784  0.42352941  0.30980392]
   [ 0.45098039  0.45098039  0.32941176]
   ..., 
   [ 0.92156863  0.86666667  0.6745098 ]
   [ 0.89803922  0.81568627  0.63137255]
   [ 0.74901961  0.66666667  0.51372549]]

  [[ 0.2627451   0.29019608  0.23921569]
   [ 0.30588235  0.3254902   0.26666667]
   [ 0.43921569  0.45098039  0.35294118]
   ..., 
   [ 0.89019608  0.80784314  0.58823529]
   [ 0.85098039  0.75294118  0.54509804]
   [ 0.72156863  0.62745098  0.45882353]]]


 [[[ 0.03921569  0.01568627  0.05490196]
   [ 0.04313725  0.02352941  0.05882353]
   [ 0.07843137  0.08627451  0.09019608]
   ..., 
   [ 0.23137255  0.27843137  0.21568627]
   [ 0.22352941  0.2745098   0.21568627]
   [ 0.20784314  0.26666667  0.23137255]]

  [[ 0.03921569  0.01568627  0.05490196]
   [ 0.04313725  0.03529412  0.05882353]
   [ 0.09803922  0.1254902   0.10980392]
   ..., 
   [ 0.21568627  0.23921569  0.18823529]
   [ 0.25882353  0.29803922  0.22745098]
   [ 0.18823529  0.24705882  0.21176471]]

  [[ 0.04705882  0.02352941  0.0627451 ]
   [ 0.04313725  0.03921569  0.05882353]
   [ 0.14901961  0.18431373  0.14901961]
   ..., 
   [ 0.18431373  0.20392157  0.16470588]
   [ 0.22352941  0.25882353  0.19215686]
   [ 0.20392157  0.25098039  0.2       ]]

  ..., 
  [[ 0.69411765  0.6627451   0.71764706]
   [ 0.70588235  0.6627451   0.72156863]
   [ 0.72156863  0.6745098   0.74509804]
   ..., 
   [ 0.6745098   0.62352941  0.68235294]
   [ 0.66666667  0.61568627  0.67058824]
   [ 0.64313725  0.58823529  0.64705882]]

  [[ 0.62352941  0.61568627  0.69019608]
   [ 0.63529412  0.61568627  0.69411765]
   [ 0.65490196  0.63529412  0.71764706]
   ..., 
   [ 0.72156863  0.69803922  0.78431373]
   [ 0.70980392  0.68627451  0.77254902]
   [ 0.69803922  0.67843137  0.76470588]]

  [[ 0.61960784  0.61960784  0.72941176]
   [ 0.62352941  0.62352941  0.73333333]
   [ 0.63921569  0.63921569  0.74509804]
   ..., 
   [ 0.70980392  0.70588235  0.82745098]
   [ 0.70196078  0.69411765  0.81568627]
   [ 0.68627451  0.68235294  0.80392157]]]


 [[[ 0.68627451  0.75686275  0.89803922]
   [ 0.6745098   0.75294118  0.9254902 ]
   [ 0.67058824  0.75686275  0.94117647]
   ..., 
   [ 0.75686275  0.80784314  0.93333333]
   [ 0.76862745  0.80784314  0.90588235]
   [ 0.76078431  0.79607843  0.89019608]]

  [[ 0.65490196  0.73333333  0.88627451]
   [ 0.64313725  0.73333333  0.90196078]
   [ 0.64313725  0.7372549   0.90980392]
   ..., 
   [ 0.70196078  0.75686275  0.88235294]
   [ 0.69803922  0.74901961  0.85098039]
   [ 0.69019608  0.73333333  0.83137255]]

  [[ 0.65490196  0.72156863  0.86666667]
   [ 0.65490196  0.72941176  0.87058824]
   [ 0.67058824  0.72941176  0.85098039]
   ..., 
   [ 0.69019608  0.74901961  0.87843137]
   [ 0.68235294  0.74117647  0.85098039]
   [ 0.67058824  0.72156863  0.82745098]]

  ..., 
  [[ 0.33333333  0.32941176  0.39607843]
   [ 0.33333333  0.32156863  0.36470588]
   [ 0.36078431  0.32941176  0.32156863]
   ..., 
   [ 0.4745098   0.44313725  0.47058824]
   [ 0.41960784  0.40392157  0.46666667]
   [ 0.45882353  0.43921569  0.50588235]]

  [[ 0.33333333  0.34117647  0.4       ]
   [ 0.32941176  0.31764706  0.34901961]
   [ 0.34117647  0.30980392  0.30588235]
   ..., 
   [ 0.30196078  0.28235294  0.32941176]
   [ 0.43137255  0.40392157  0.47058824]
   [ 0.44705882  0.41960784  0.48627451]]

  [[ 0.32156863  0.32941176  0.37647059]
   [ 0.29411765  0.29019608  0.32156863]
   [ 0.22352941  0.19607843  0.21568627]
   ..., 
   [ 0.30196078  0.26666667  0.30588235]
   [ 0.35686275  0.30588235  0.35294118]
   [ 0.35686275  0.30588235  0.35294118]]]]
[[[[ 0.54901961  0.49019608  0.45098039]
   [ 0.57254902  0.50980392  0.47843137]
   [ 0.56078431  0.49803922  0.47843137]
   ..., 
   [ 0.66666667  0.56862745  0.51372549]
   [ 0.69019608  0.58823529  0.5254902 ]
   [ 0.66666667  0.57647059  0.52156863]]

  [[ 0.4745098   0.42352941  0.50588235]
   [ 0.50980392  0.4627451   0.54509804]
   [ 0.5254902   0.4745098   0.56078431]
   ..., 
   [ 0.63921569  0.55294118  0.61568627]
   [ 0.66666667  0.57254902  0.63137255]
   [ 0.66666667  0.58039216  0.63137255]]

  [[ 0.59607843  0.54509804  0.68235294]
   [ 0.61568627  0.56862745  0.70196078]
   [ 0.60784314  0.56078431  0.68627451]
   ..., 
   [ 0.69411765  0.60392157  0.75686275]
   [ 0.70980392  0.61176471  0.76078431]
   [ 0.71764706  0.62745098  0.76078431]]

  ..., 
  [[ 0.49019608  0.43137255  0.4       ]
   [ 0.50588235  0.43921569  0.40392157]
   [ 0.29803922  0.2627451   0.18431373]
   ..., 
   [ 0.65882353  0.5372549   0.47058824]
   [ 0.61960784  0.49411765  0.40392157]
   [ 0.57254902  0.45490196  0.34117647]]

  [[ 0.33333333  0.30196078  0.2745098 ]
   [ 0.36862745  0.31764706  0.27843137]
   [ 0.29019608  0.25490196  0.17647059]
   ..., 
   [ 0.63529412  0.51764706  0.41568627]
   [ 0.65098039  0.5254902   0.39215686]
   [ 0.61960784  0.50196078  0.36078431]]

  [[ 0.49019608  0.43921569  0.43529412]
   [ 0.50980392  0.44313725  0.43529412]
   [ 0.41176471  0.35686275  0.29411765]
   ..., 
   [ 0.51764706  0.41568627  0.30588235]
   [ 0.50980392  0.39607843  0.25098039]
   [ 0.55686275  0.45098039  0.30588235]]]


 [[[ 0.39215686  0.42745098  0.32941176]
   [ 0.47843137  0.49411765  0.42745098]
   [ 0.34117647  0.34117647  0.29803922]
   ..., 
   [ 0.29411765  0.30588235  0.27058824]
   [ 0.2745098   0.28627451  0.25098039]
   [ 0.2745098   0.28627451  0.25098039]]

  [[ 0.3372549   0.38823529  0.27843137]
   [ 0.29803922  0.32941176  0.25882353]
   [ 0.23529412  0.25098039  0.21176471]
   ..., 
   [ 0.30588235  0.31764706  0.28235294]
   [ 0.29803922  0.30980392  0.2745098 ]
   [ 0.32156863  0.33333333  0.29803922]]

  [[ 0.32941176  0.39215686  0.28627451]
   [ 0.3254902   0.37254902  0.29411765]
   [ 0.30196078  0.3372549   0.28235294]
   ..., 
   [ 0.29019608  0.30196078  0.26666667]
   [ 0.28627451  0.29803922  0.2627451 ]
   [ 0.3254902   0.3372549   0.30196078]]

  ..., 
  [[ 0.25098039  0.30196078  0.30980392]
   [ 0.47843137  0.52156863  0.56470588]
   [ 0.5254902   0.56862745  0.61176471]
   ..., 
   [ 0.41176471  0.48235294  0.47058824]
   [ 0.32941176  0.40392157  0.35686275]
   [ 0.23529412  0.34509804  0.24705882]]

  [[ 0.17254902  0.2         0.21960784]
   [ 0.30588235  0.32941176  0.36862745]
   [ 0.37647059  0.39607843  0.43137255]
   ..., 
   [ 0.57647059  0.64705882  0.69803922]
   [ 0.49411765  0.56078431  0.58431373]
   [ 0.36862745  0.45882353  0.44313725]]

  [[ 0.14117647  0.1372549   0.15294118]
   [ 0.23137255  0.22745098  0.25882353]
   [ 0.32156863  0.31764706  0.33333333]
   ..., 
   [ 0.5254902   0.6         0.62745098]
   [ 0.54117647  0.59607843  0.61960784]
   [ 0.50980392  0.58039216  0.58823529]]]


 [[[ 0.0745098   0.1254902   0.05882353]
   [ 0.08235294  0.14901961  0.08235294]
   [ 0.10588235  0.19215686  0.1254902 ]
   ..., 
   [ 0.29411765  0.48627451  0.51372549]
   [ 0.29803922  0.48627451  0.50980392]
   [ 0.27843137  0.4627451   0.48627451]]

  [[ 0.09019608  0.12156863  0.05490196]
   [ 0.08235294  0.11764706  0.04705882]
   [ 0.09019608  0.1372549   0.05490196]
   ..., 
   [ 0.28235294  0.4627451   0.49411765]
   [ 0.29411765  0.46666667  0.48627451]
   [ 0.26666667  0.43529412  0.44705882]]

  [[ 0.09411765  0.14509804  0.06666667]
   [ 0.08627451  0.1372549   0.0627451 ]
   [ 0.09411765  0.14117647  0.07058824]
   ..., 
   [ 0.25098039  0.42745098  0.43921569]
   [ 0.2627451   0.42745098  0.43529412]
   [ 0.25098039  0.41176471  0.41176471]]

  ..., 
  [[ 0.24313725  0.18039216  0.09019608]
   [ 0.23529412  0.18039216  0.10588235]
   [ 0.21568627  0.18823529  0.10980392]
   ..., 
   [ 0.05098039  0.02352941  0.01568627]
   [ 0.04705882  0.05490196  0.03137255]
   [ 0.09803922  0.15686275  0.11764706]]

  [[ 0.24705882  0.20784314  0.11764706]
   [ 0.19215686  0.17647059  0.08627451]
   [ 0.17647059  0.18039216  0.09019608]
   ..., 
   [ 0.11372549  0.1372549   0.12156863]
   [ 0.11764706  0.16470588  0.14509804]
   [ 0.10588235  0.19607843  0.16862745]]

  [[ 0.27058824  0.20392157  0.11372549]
   [ 0.19215686  0.14901961  0.07843137]
   [ 0.21176471  0.18039216  0.10588235]
   ..., 
   [ 0.25882353  0.34509804  0.33333333]
   [ 0.15686275  0.26666667  0.25098039]
   [ 0.11372549  0.24313725  0.22745098]]]


 ..., 
 [[[ 0.1372549   0.69803922  0.92156863]
   [ 0.15686275  0.69019608  0.9372549 ]
   [ 0.16470588  0.69019608  0.94509804]
   ..., 
   [ 0.38823529  0.69411765  0.85882353]
   [ 0.30980392  0.57647059  0.77254902]
   [ 0.34901961  0.58039216  0.74117647]]

  [[ 0.22352941  0.71372549  0.91764706]
   [ 0.17254902  0.72156863  0.98039216]
   [ 0.19607843  0.71764706  0.94117647]
   ..., 
   [ 0.61176471  0.71372549  0.78431373]
   [ 0.55294118  0.69411765  0.80784314]
   [ 0.45490196  0.58431373  0.68627451]]

  [[ 0.38431373  0.77254902  0.92941176]
   [ 0.25098039  0.74117647  0.98823529]
   [ 0.27058824  0.75294118  0.96078431]
   ..., 
   [ 0.7372549   0.76470588  0.80784314]
   [ 0.46666667  0.52941176  0.57647059]
   [ 0.23921569  0.30980392  0.35294118]]

  ..., 
  [[ 0.28627451  0.30980392  0.30196078]
   [ 0.20784314  0.24705882  0.26666667]
   [ 0.21176471  0.26666667  0.31372549]
   ..., 
   [ 0.06666667  0.15686275  0.25098039]
   [ 0.08235294  0.14117647  0.2       ]
   [ 0.12941176  0.18823529  0.19215686]]

  [[ 0.23921569  0.26666667  0.29411765]
   [ 0.21568627  0.2745098   0.3372549 ]
   [ 0.22352941  0.30980392  0.40392157]
   ..., 
   [ 0.09411765  0.18823529  0.28235294]
   [ 0.06666667  0.1372549   0.20784314]
   [ 0.02745098  0.09019608  0.1254902 ]]

  [[ 0.17254902  0.21960784  0.28627451]
   [ 0.18039216  0.25882353  0.34509804]
   [ 0.19215686  0.30196078  0.41176471]
   ..., 
   [ 0.10588235  0.20392157  0.30196078]
   [ 0.08235294  0.16862745  0.25882353]
   [ 0.04705882  0.12156863  0.19607843]]]


 [[[ 0.74117647  0.82745098  0.94117647]
   [ 0.72941176  0.81568627  0.9254902 ]
   [ 0.7254902   0.81176471  0.92156863]
   ..., 
   [ 0.68627451  0.76470588  0.87843137]
   [ 0.6745098   0.76078431  0.87058824]
   [ 0.6627451   0.76078431  0.8627451 ]]

  [[ 0.76078431  0.82352941  0.9372549 ]
   [ 0.74901961  0.81176471  0.9254902 ]
   [ 0.74509804  0.80784314  0.92156863]
   ..., 
   [ 0.67843137  0.75294118  0.8627451 ]
   [ 0.67058824  0.74901961  0.85490196]
   [ 0.65490196  0.74509804  0.84705882]]

  [[ 0.81568627  0.85882353  0.95686275]
   [ 0.80392157  0.84705882  0.94117647]
   [ 0.8         0.84313725  0.9372549 ]
   ..., 
   [ 0.68627451  0.74901961  0.85098039]
   [ 0.6745098   0.74509804  0.84705882]
   [ 0.6627451   0.74901961  0.84313725]]

  ..., 
  [[ 0.81176471  0.78039216  0.70980392]
   [ 0.79607843  0.76470588  0.68627451]
   [ 0.79607843  0.76862745  0.67843137]
   ..., 
   [ 0.52941176  0.51764706  0.49803922]
   [ 0.63529412  0.61960784  0.58823529]
   [ 0.65882353  0.63921569  0.59215686]]

  [[ 0.77647059  0.74509804  0.66666667]
   [ 0.74117647  0.70980392  0.62352941]
   [ 0.70588235  0.6745098   0.57647059]
   ..., 
   [ 0.69803922  0.67058824  0.62745098]
   [ 0.68627451  0.6627451   0.61176471]
   [ 0.68627451  0.6627451   0.60392157]]

  [[ 0.77647059  0.74117647  0.67843137]
   [ 0.74117647  0.70980392  0.63529412]
   [ 0.69803922  0.66666667  0.58431373]
   ..., 
   [ 0.76470588  0.72156863  0.6627451 ]
   [ 0.76862745  0.74117647  0.67058824]
   [ 0.76470588  0.74509804  0.67058824]]]


 [[[ 0.89803922  0.89803922  0.9372549 ]
   [ 0.9254902   0.92941176  0.96862745]
   [ 0.91764706  0.9254902   0.96862745]
   ..., 
   [ 0.85098039  0.85882353  0.91372549]
   [ 0.86666667  0.8745098   0.91764706]
   [ 0.87058824  0.8745098   0.91372549]]

  [[ 0.87058824  0.86666667  0.89803922]
   [ 0.9372549   0.9372549   0.97647059]
   [ 0.91372549  0.91764706  0.96470588]
   ..., 
   [ 0.8745098   0.8745098   0.9254902 ]
   [ 0.89019608  0.89411765  0.93333333]
   [ 0.82352941  0.82745098  0.8627451 ]]

  [[ 0.83529412  0.80784314  0.82745098]
   [ 0.91764706  0.90980392  0.9372549 ]
   [ 0.90588235  0.91372549  0.95686275]
   ..., 
   [ 0.8627451   0.8627451   0.90980392]
   [ 0.8627451   0.85882353  0.90980392]
   [ 0.79215686  0.79607843  0.84313725]]

  ..., 
  [[ 0.58823529  0.56078431  0.52941176]
   [ 0.54901961  0.52941176  0.49803922]
   [ 0.51764706  0.49803922  0.47058824]
   ..., 
   [ 0.87843137  0.87058824  0.85490196]
   [ 0.90196078  0.89411765  0.88235294]
   [ 0.94509804  0.94509804  0.93333333]]

  [[ 0.5372549   0.51764706  0.49411765]
   [ 0.50980392  0.49803922  0.47058824]
   [ 0.49019608  0.4745098   0.45098039]
   ..., 
   [ 0.70980392  0.70588235  0.69803922]
   [ 0.79215686  0.78823529  0.77647059]
   [ 0.83137255  0.82745098  0.81176471]]

  [[ 0.47843137  0.46666667  0.44705882]
   [ 0.4627451   0.45490196  0.43137255]
   [ 0.47058824  0.45490196  0.43529412]
   ..., 
   [ 0.70196078  0.69411765  0.67843137]
   [ 0.64313725  0.64313725  0.63529412]
   [ 0.63921569  0.63921569  0.63137255]]]]
[[[[ 0.61960784  0.43921569  0.19215686]
   [ 0.62352941  0.43529412  0.18431373]
   [ 0.64705882  0.45490196  0.2       ]
   ..., 
   [ 0.5372549   0.37254902  0.14117647]
   [ 0.49411765  0.35686275  0.14117647]
   [ 0.45490196  0.33333333  0.12941176]]

  [[ 0.59607843  0.43921569  0.2       ]
   [ 0.59215686  0.43137255  0.15686275]
   [ 0.62352941  0.44705882  0.17647059]
   ..., 
   [ 0.53333333  0.37254902  0.12156863]
   [ 0.49019608  0.35686275  0.1254902 ]
   [ 0.46666667  0.34509804  0.13333333]]

  [[ 0.59215686  0.43137255  0.18431373]
   [ 0.59215686  0.42745098  0.12941176]
   [ 0.61960784  0.43529412  0.14117647]
   ..., 
   [ 0.54509804  0.38431373  0.13333333]
   [ 0.50980392  0.37254902  0.13333333]
   [ 0.47058824  0.34901961  0.12941176]]

  ..., 
  [[ 0.26666667  0.48627451  0.69411765]
   [ 0.16470588  0.39215686  0.58039216]
   [ 0.12156863  0.34509804  0.5372549 ]
   ..., 
   [ 0.14901961  0.38039216  0.57254902]
   [ 0.05098039  0.25098039  0.42352941]
   [ 0.15686275  0.33333333  0.49803922]]

  [[ 0.23921569  0.45490196  0.65882353]
   [ 0.19215686  0.4         0.58039216]
   [ 0.1372549   0.33333333  0.51764706]
   ..., 
   [ 0.10196078  0.32156863  0.50980392]
   [ 0.11372549  0.32156863  0.49411765]
   [ 0.07843137  0.25098039  0.41960784]]

  [[ 0.21176471  0.41960784  0.62745098]
   [ 0.21960784  0.41176471  0.58431373]
   [ 0.17647059  0.34901961  0.51764706]
   ..., 
   [ 0.09411765  0.30196078  0.48627451]
   [ 0.13333333  0.32941176  0.50588235]
   [ 0.08235294  0.2627451   0.43137255]]]


 [[[ 0.92156863  0.92156863  0.92156863]
   [ 0.90588235  0.90588235  0.90588235]
   [ 0.90980392  0.90980392  0.90980392]
   ..., 
   [ 0.91372549  0.91372549  0.91372549]
   [ 0.91372549  0.91372549  0.91372549]
   [ 0.90980392  0.90980392  0.90980392]]

  [[ 0.93333333  0.93333333  0.93333333]
   [ 0.92156863  0.92156863  0.92156863]
   [ 0.92156863  0.92156863  0.92156863]
   ..., 
   [ 0.9254902   0.9254902   0.9254902 ]
   [ 0.9254902   0.9254902   0.9254902 ]
   [ 0.92156863  0.92156863  0.92156863]]

  [[ 0.92941176  0.92941176  0.92941176]
   [ 0.91764706  0.91764706  0.91764706]
   [ 0.91764706  0.91764706  0.91764706]
   ..., 
   [ 0.92156863  0.92156863  0.92156863]
   [ 0.92156863  0.92156863  0.92156863]
   [ 0.91764706  0.91764706  0.91764706]]

  ..., 
  [[ 0.34117647  0.38823529  0.34901961]
   [ 0.16862745  0.2         0.14509804]
   [ 0.0745098   0.09019608  0.04313725]
   ..., 
   [ 0.6627451   0.72156863  0.70196078]
   [ 0.71372549  0.77254902  0.75686275]
   [ 0.7372549   0.79215686  0.78823529]]

  [[ 0.32156863  0.37647059  0.32156863]
   [ 0.18039216  0.22352941  0.14117647]
   [ 0.14117647  0.17254902  0.08627451]
   ..., 
   [ 0.68235294  0.74117647  0.71764706]
   [ 0.7254902   0.78431373  0.76862745]
   [ 0.73333333  0.79215686  0.78431373]]

  [[ 0.33333333  0.39607843  0.3254902 ]
   [ 0.24313725  0.29411765  0.18823529]
   [ 0.22745098  0.2627451   0.14901961]
   ..., 
   [ 0.65882353  0.71764706  0.69803922]
   [ 0.70588235  0.76470588  0.74901961]
   [ 0.72941176  0.78431373  0.78039216]]]


 [[[ 0.61960784  0.74509804  0.87058824]
   [ 0.61960784  0.73333333  0.85490196]
   [ 0.54509804  0.65098039  0.76078431]
   ..., 
   [ 0.89411765  0.90588235  0.91764706]
   [ 0.92941176  0.9372549   0.95294118]
   [ 0.93333333  0.94509804  0.96470588]]

  [[ 0.66666667  0.78431373  0.89803922]
   [ 0.6745098   0.78039216  0.88627451]
   [ 0.59215686  0.69019608  0.78823529]
   ..., 
   [ 0.90980392  0.90980392  0.9254902 ]
   [ 0.96470588  0.96470588  0.98039216]
   [ 0.96470588  0.96862745  0.98431373]]

  [[ 0.68235294  0.78823529  0.88235294]
   [ 0.69019608  0.78431373  0.87058824]
   [ 0.61568627  0.70196078  0.78039216]
   ..., 
   [ 0.90196078  0.89803922  0.90980392]
   [ 0.98039216  0.97647059  0.98431373]
   [ 0.96078431  0.95686275  0.96862745]]

  ..., 
  [[ 0.12156863  0.15686275  0.17647059]
   [ 0.11764706  0.15294118  0.17254902]
   [ 0.10196078  0.1372549   0.15686275]
   ..., 
   [ 0.14509804  0.15686275  0.18039216]
   [ 0.03529412  0.05098039  0.05490196]
   [ 0.01568627  0.02745098  0.01960784]]

  [[ 0.09019608  0.13333333  0.15294118]
   [ 0.10588235  0.14901961  0.16862745]
   [ 0.09803922  0.14117647  0.16078431]
   ..., 
   [ 0.0745098   0.07843137  0.09411765]
   [ 0.01568627  0.02352941  0.01176471]
   [ 0.01960784  0.02745098  0.01176471]]

  [[ 0.10980392  0.16078431  0.18431373]
   [ 0.11764706  0.16862745  0.19607843]
   [ 0.1254902   0.17647059  0.20392157]
   ..., 
   [ 0.01960784  0.02352941  0.03137255]
   [ 0.01568627  0.01960784  0.01176471]
   [ 0.02745098  0.03137255  0.02745098]]]


 ..., 
 [[[ 0.07843137  0.05882353  0.04705882]
   [ 0.0745098   0.05490196  0.04313725]
   [ 0.05882353  0.05490196  0.04313725]
   ..., 
   [ 0.03921569  0.03529412  0.02745098]
   [ 0.04705882  0.04313725  0.03529412]
   [ 0.05098039  0.04705882  0.03921569]]

  [[ 0.08235294  0.0627451   0.05098039]
   [ 0.07843137  0.0627451   0.05098039]
   [ 0.07058824  0.06666667  0.04705882]
   ..., 
   [ 0.03921569  0.03529412  0.02745098]
   [ 0.03921569  0.03529412  0.02745098]
   [ 0.04705882  0.04313725  0.03529412]]

  [[ 0.08235294  0.0627451   0.05098039]
   [ 0.08235294  0.06666667  0.04705882]
   [ 0.07843137  0.07058824  0.04313725]
   ..., 
   [ 0.04705882  0.04313725  0.03529412]
   [ 0.04705882  0.04313725  0.03529412]
   [ 0.05098039  0.04705882  0.03921569]]

  ..., 
  [[ 0.12941176  0.09803922  0.05098039]
   [ 0.13333333  0.10196078  0.05882353]
   [ 0.13333333  0.10196078  0.05882353]
   ..., 
   [ 0.10980392  0.09803922  0.20392157]
   [ 0.11372549  0.09803922  0.22745098]
   [ 0.09019608  0.07843137  0.16470588]]

  [[ 0.12941176  0.09803922  0.05490196]
   [ 0.13333333  0.10196078  0.05882353]
   [ 0.13333333  0.10196078  0.05882353]
   ..., 
   [ 0.10588235  0.09411765  0.20392157]
   [ 0.10588235  0.09411765  0.21960784]
   [ 0.09803922  0.08627451  0.18431373]]

  [[ 0.12156863  0.09019608  0.04705882]
   [ 0.1254902   0.09411765  0.05098039]
   [ 0.12941176  0.09803922  0.05490196]
   ..., 
   [ 0.09411765  0.09019608  0.19607843]
   [ 0.10196078  0.09019608  0.20784314]
   [ 0.09803922  0.07843137  0.18431373]]]


 [[[ 0.09803922  0.15686275  0.04705882]
   [ 0.05882353  0.14117647  0.01176471]
   [ 0.09019608  0.16078431  0.07058824]
   ..., 
   [ 0.23921569  0.32156863  0.30588235]
   [ 0.36078431  0.44313725  0.43921569]
   [ 0.29411765  0.34901961  0.36078431]]

  [[ 0.04705882  0.09803922  0.02352941]
   [ 0.07843137  0.14509804  0.02745098]
   [ 0.09411765  0.14117647  0.05882353]
   ..., 
   [ 0.45098039  0.5254902   0.54117647]
   [ 0.58431373  0.65882353  0.69411765]
   [ 0.40784314  0.45882353  0.51372549]]

  [[ 0.04705882  0.09803922  0.04313725]
   [ 0.05882353  0.11372549  0.02352941]
   [ 0.13333333  0.15686275  0.09411765]
   ..., 
   [ 0.60392157  0.6745098   0.71372549]
   [ 0.61568627  0.68627451  0.75294118]
   [ 0.45490196  0.50588235  0.59215686]]

  ..., 
  [[ 0.39215686  0.50588235  0.31764706]
   [ 0.40392157  0.51764706  0.32941176]
   [ 0.40784314  0.5254902   0.3372549 ]
   ..., 
   [ 0.38039216  0.50196078  0.32941176]
   [ 0.38431373  0.49411765  0.32941176]
   [ 0.35686275  0.4745098   0.30980392]]

  [[ 0.40392157  0.51764706  0.3254902 ]
   [ 0.40784314  0.51372549  0.3254902 ]
   [ 0.41960784  0.52941176  0.34117647]
   ..., 
   [ 0.39607843  0.51764706  0.34117647]
   [ 0.38823529  0.49803922  0.32941176]
   [ 0.36078431  0.4745098   0.30980392]]

  [[ 0.37254902  0.49411765  0.30588235]
   [ 0.37254902  0.48235294  0.29803922]
   [ 0.39607843  0.50196078  0.31764706]
   ..., 
   [ 0.36470588  0.48627451  0.31372549]
   [ 0.37254902  0.48235294  0.31764706]
   [ 0.36078431  0.47058824  0.31372549]]]


 [[[ 0.28627451  0.30588235  0.29411765]
   [ 0.38431373  0.40392157  0.44313725]
   [ 0.38823529  0.41568627  0.44705882]
   ..., 
   [ 0.52941176  0.58823529  0.59607843]
   [ 0.52941176  0.58431373  0.60392157]
   [ 0.79607843  0.84313725  0.8745098 ]]

  [[ 0.27058824  0.28627451  0.2745098 ]
   [ 0.32941176  0.34901961  0.38039216]
   [ 0.26666667  0.29411765  0.31764706]
   ..., 
   [ 0.33333333  0.37254902  0.34901961]
   [ 0.27843137  0.32156863  0.31372549]
   [ 0.47058824  0.52156863  0.52941176]]

  [[ 0.27058824  0.28627451  0.2745098 ]
   [ 0.35294118  0.37254902  0.39215686]
   [ 0.24313725  0.27843137  0.29019608]
   ..., 
   [ 0.29019608  0.31764706  0.2745098 ]
   [ 0.20784314  0.24313725  0.21176471]
   [ 0.24313725  0.29019608  0.27058824]]

  ..., 
  [[ 0.48235294  0.50196078  0.37647059]
   [ 0.51764706  0.51764706  0.4       ]
   [ 0.50588235  0.50196078  0.39215686]
   ..., 
   [ 0.42352941  0.41960784  0.34509804]
   [ 0.24313725  0.23529412  0.21568627]
   [ 0.10588235  0.10588235  0.10980392]]

  [[ 0.45098039  0.4745098   0.35686275]
   [ 0.48235294  0.48627451  0.37254902]
   [ 0.50588235  0.49411765  0.38823529]
   ..., 
   [ 0.45098039  0.45490196  0.36862745]
   [ 0.25882353  0.25490196  0.23137255]
   [ 0.10588235  0.10588235  0.10588235]]

  [[ 0.45490196  0.47058824  0.35294118]
   [ 0.4745098   0.47843137  0.36862745]
   [ 0.50588235  0.50196078  0.39607843]
   ..., 
   [ 0.45490196  0.45098039  0.36862745]
   [ 0.26666667  0.25490196  0.22745098]
   [ 0.10588235  0.10196078  0.10196078]]]]

In [7]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper

# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))


    

In [8]:

    

import tensorflow as tf

def neural_net_image_input(image_shape):
    """
    Return a Tensor for a batch of image input
    : image_shape: Shape of the images
    : return: Tensor for image input.
    """
    # TODO: Implement Function
    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')


def neural_net_label_input(n_classes):
    """
    Return a Tensor for a batch of label input
    : n_classes: Number of classes
    : return: Tensor for label input.
    """
    # TODO: Implement Function
    return tf.placeholder(tf.float32, [None, n_classes], name='y')


def neural_net_keep_prob_input():
    """
    Return a Tensor for keep probability
    : return: Tensor for keep probability.
    """
    # TODO: Implement Function
    return tf.placeholder(tf.float32, name='keep_prob')


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)


    

    




Image Input Tests Passed.
Label Input Tests Passed.
Keep Prob Tests Passed.

In [9]:

    

def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
    """
    Apply convolution then max pooling to x_tensor
    :param x_tensor: TensorFlow Tensor
    :param conv_num_outputs: Number of outputs for the convolutional layer
    :param conv_ksize: kernel size 2-D Tuple for the convolutional layer
    :param conv_strides: Stride 2-D Tuple for convolution
    :param pool_ksize: kernel size 2-D Tuple for pool
    :param pool_strides: Stride 2-D Tuple for pool
    : return: A tensor that represents convolution and max pooling of x_tensor
    """
    # TODO: Implement Function
    kernel = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], int(x_tensor.shape[3]), conv_num_outputs], mean=0.0, stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([conv_num_outputs], mean=0.0, stddev=0.1))
    conv = tf.nn.conv2d(x_tensor, kernel, [1, conv_strides[0], conv_strides[1], 1], 'SAME')
    conv = tf.nn.bias_add(conv, bias)
    conv = tf.nn.relu(conv)
    pool = tf.nn.max_pool(conv, [1, pool_ksize[0], pool_ksize[1], 1], [1, pool_strides[0], pool_strides[1], 1], 'SAME')
    return pool


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)


    

    




Tests Passed

In [10]:

    

def flatten(x_tensor):
    """
    Flatten x_tensor to (Batch Size, Flattened Image Size)
    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
    : return: A tensor of size (Batch Size, Flattened Image Size).
    """
    # TODO: Implement Function
    dimensions = x_tensor.get_shape().as_list()
    return tf.reshape(x_tensor, [-1, dimensions[1]*dimensions[2]*dimensions[3]])


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)


    

    




Tests Passed

In [11]:

    

def fully_conn(x_tensor, num_outputs):
    """
    Apply a fully connected layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    # TODO: Implement Function
    dims = x_tensor.get_shape().as_list()
    weights = tf.Variable(tf.truncated_normal([dims[1], num_outputs], mean=0.0, stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([num_outputs], mean=0.0, stddev=0.1))
    layer = tf.add(tf.matmul(x_tensor, weights), bias)
    layer = tf.nn.relu(layer)
    return layer


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)


    

    




Tests Passed

In [12]:

    

def output(x_tensor, num_outputs):
    """
    Apply a output layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    # TODO: Implement Function
    dims = x_tensor.get_shape().as_list()
    weights = tf.Variable(tf.truncated_normal([dims[1], num_outputs], mean=0.0, stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([num_outputs], mean=0.0, stddev=0.1))
    layer = tf.add(tf.matmul(x_tensor, weights), bias)
    return layer


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)


    

    




Tests Passed

In [13]:

    

def conv_net(x, keep_prob):
    """
    Create a convolutional neural network model
    : x: Placeholder tensor that holds image data.
    : keep_prob: Placeholder tensor that hold dropout keep probability.
    : return: Tensor that represents logits
    """
    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
    #    Play around with different number of outputs, kernel size and stride
    # Function Definition from Above:
    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
    
    conv = conv2d_maxpool(x, 64, [4,4], [2,2], [4,4], [2,2])
    conv = conv2d_maxpool(conv, 128, [4,4], [2,2], [4,4], [2,2])
    conv = conv2d_maxpool(conv, 254, [4,4], [2,2], [4,4], [2,2])
    

    # TODO: Apply a Flatten Layer
    # Function Definition from Above:
    #   flatten(x_tensor)
    flattened_x = flatten(conv)
    

    # TODO: Apply 1, 2, or 3 Fully Connected Layers
    #    Play around with different number of outputs
    # Function Definition from Above:
    #   fully_conn(x_tensor, num_outputs)
    full_layer = fully_conn(flattened_x, 2048)
    full_layer = tf.nn.dropout(full_layer, keep_prob)
    full_layer = fully_conn(full_layer, 2048)
    full_layer = tf.nn.dropout(full_layer, keep_prob)
    
    
    # TODO: Apply an Output Layer
    #    Set this to the number of classes
    # Function Definition from Above:
    #   output(x_tensor, num_outputs)
    output_layer = output(full_layer, 10)
    
    
    # TODO: return output
    return output_layer


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""

##############################
## Build the Neural Network ##
##############################

# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()

# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()

# Model
logits = conv_net(x, keep_prob)

# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')

# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)

# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')

tests.test_conv_net(conv_net)


    

    




Neural Network Built!

In [14]:

    

def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
    """
    Optimize the session on a batch of images and labels
    : session: Current TensorFlow session
    : optimizer: TensorFlow optimizer function
    : keep_probability: keep probability
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    """
    # TODO: Implement Function
    session.run(optimizer, feed_dict = {x: feature_batch, y: label_batch, keep_prob: keep_probability})
        


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)


    

    




Tests Passed

In [15]:

    

def print_stats(session, feature_batch, label_batch, cost, accuracy):
    """
    Print information about loss and validation accuracy
    : session: Current TensorFlow session
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    : cost: TensorFlow cost function
    : accuracy: TensorFlow accuracy function
    """
    # TODO: Implement Function
    print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}))
    print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))


    

In [16]:

    

# TODO: Tune Parameters
epochs = 50
batch_size = 512
keep_probability = 0.5


    

In [17]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        batch_i = 1
        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
        print_stats(sess, batch_features, batch_labels, cost, accuracy)


    

    




Checking the Training on a Single Batch...
Epoch  1, CIFAR-10 Batch 1:  2.19404
0.178
Epoch  2, CIFAR-10 Batch 1:  2.01839
0.2826
Epoch  3, CIFAR-10 Batch 1:  1.8744
0.3326
Epoch  4, CIFAR-10 Batch 1:  1.77784
0.3798
Epoch  5, CIFAR-10 Batch 1:  1.68298
0.3912
Epoch  6, CIFAR-10 Batch 1:  1.61434
0.421
Epoch  7, CIFAR-10 Batch 1:  1.50713
0.4354
Epoch  8, CIFAR-10 Batch 1:  1.42413
0.461
Epoch  9, CIFAR-10 Batch 1:  1.36696
0.4608
Epoch 10, CIFAR-10 Batch 1:  1.27398
0.4808
Epoch 11, CIFAR-10 Batch 1:  1.20977
0.4884
Epoch 12, CIFAR-10 Batch 1:  1.19683
0.4826
Epoch 13, CIFAR-10 Batch 1:  1.07412
0.5088
Epoch 14, CIFAR-10 Batch 1:  1.02847
0.5046
Epoch 15, CIFAR-10 Batch 1:  0.975882
0.5034
Epoch 16, CIFAR-10 Batch 1:  0.882206
0.5124
Epoch 17, CIFAR-10 Batch 1:  0.831756
0.5156
Epoch 18, CIFAR-10 Batch 1:  0.831632
0.503
Epoch 19, CIFAR-10 Batch 1:  0.763783
0.5188
Epoch 20, CIFAR-10 Batch 1:  0.77896
0.4796
Epoch 21, CIFAR-10 Batch 1:  0.687507
0.5186
Epoch 22, CIFAR-10 Batch 1:  0.668112
0.5186
Epoch 23, CIFAR-10 Batch 1:  0.656766
0.5256
Epoch 24, CIFAR-10 Batch 1:  0.62328
0.5286
Epoch 25, CIFAR-10 Batch 1:  0.534758
0.5292
Epoch 26, CIFAR-10 Batch 1:  0.515915
0.5184
Epoch 27, CIFAR-10 Batch 1:  0.54065
0.4982
Epoch 28, CIFAR-10 Batch 1:  0.413432
0.5292
Epoch 29, CIFAR-10 Batch 1:  0.38066
0.5452
Epoch 30, CIFAR-10 Batch 1:  0.335785
0.5344
Epoch 31, CIFAR-10 Batch 1:  0.314171
0.5272
Epoch 32, CIFAR-10 Batch 1:  0.290111
0.5268
Epoch 33, CIFAR-10 Batch 1:  0.295798
0.5142
Epoch 34, CIFAR-10 Batch 1:  0.359277
0.5208
Epoch 35, CIFAR-10 Batch 1:  0.332696
0.513
Epoch 36, CIFAR-10 Batch 1:  0.365781
0.5138
Epoch 37, CIFAR-10 Batch 1:  0.287645
0.531
Epoch 38, CIFAR-10 Batch 1:  0.234741
0.5402
Epoch 39, CIFAR-10 Batch 1:  0.300134
0.5094
Epoch 40, CIFAR-10 Batch 1:  0.359654
0.494
Epoch 41, CIFAR-10 Batch 1:  0.307713
0.489
Epoch 42, CIFAR-10 Batch 1:  0.239576
0.5196
Epoch 43, CIFAR-10 Batch 1:  0.194945
0.5416
Epoch 44, CIFAR-10 Batch 1:  0.155065
0.5496
Epoch 45, CIFAR-10 Batch 1:  0.143629
0.5358
Epoch 46, CIFAR-10 Batch 1:  0.203878
0.5072
Epoch 47, CIFAR-10 Batch 1:  0.24894
0.4978
Epoch 48, CIFAR-10 Batch 1:  0.169556
0.5214
Epoch 49, CIFAR-10 Batch 1:  0.156673
0.5148
Epoch 50, CIFAR-10 Batch 1:  0.151767
0.5134

In [18]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'

print('Training...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        # Loop over all batches
        n_batches = 5
        for batch_i in range(1, n_batches + 1):
            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
            print_stats(sess, batch_features, batch_labels, cost, accuracy)
            
    # Save Model
    saver = tf.train.Saver()
    save_path = saver.save(sess, save_model_path)


    

    




Training...
Epoch  1, CIFAR-10 Batch 1:  2.17807
0.1908
Epoch  1, CIFAR-10 Batch 2:  1.95179
0.2742
Epoch  1, CIFAR-10 Batch 3:  1.84225
0.3094
Epoch  1, CIFAR-10 Batch 4:  1.76153
0.3342
Epoch  1, CIFAR-10 Batch 5:  1.71295
0.3524
Epoch  2, CIFAR-10 Batch 1:  1.69944
0.3832
Epoch  2, CIFAR-10 Batch 2:  1.65941
0.3978
Epoch  2, CIFAR-10 Batch 3:  1.51846
0.409
Epoch  2, CIFAR-10 Batch 4:  1.49259
0.4274
Epoch  2, CIFAR-10 Batch 5:  1.45563
0.4346
Epoch  3, CIFAR-10 Batch 1:  1.47814
0.4674
Epoch  3, CIFAR-10 Batch 2:  1.46239
0.4684
Epoch  3, CIFAR-10 Batch 3:  1.31846
0.469
Epoch  3, CIFAR-10 Batch 4:  1.34742
0.484
Epoch  3, CIFAR-10 Batch 5:  1.31908
0.4872
Epoch  4, CIFAR-10 Batch 1:  1.37959
0.4954
Epoch  4, CIFAR-10 Batch 2:  1.34995
0.4982
Epoch  4, CIFAR-10 Batch 3:  1.20967
0.504
Epoch  4, CIFAR-10 Batch 4:  1.23697
0.5106
Epoch  4, CIFAR-10 Batch 5:  1.22635
0.5206
Epoch  5, CIFAR-10 Batch 1:  1.29317
0.5152
Epoch  5, CIFAR-10 Batch 2:  1.25461
0.5356
Epoch  5, CIFAR-10 Batch 3:  1.12318
0.5244
Epoch  5, CIFAR-10 Batch 4:  1.15061
0.5388
Epoch  5, CIFAR-10 Batch 5:  1.1389
0.5452
Epoch  6, CIFAR-10 Batch 1:  1.19491
0.5548
Epoch  6, CIFAR-10 Batch 2:  1.19241
0.543
Epoch  6, CIFAR-10 Batch 3:  1.0395
0.5494
Epoch  6, CIFAR-10 Batch 4:  1.08373
0.55
Epoch  6, CIFAR-10 Batch 5:  1.05959
0.5692
Epoch  7, CIFAR-10 Batch 1:  1.16406
0.5494
Epoch  7, CIFAR-10 Batch 2:  1.08746
0.5768
Epoch  7, CIFAR-10 Batch 3:  0.989666
0.5748
Epoch  7, CIFAR-10 Batch 4:  1.00688
0.5702
Epoch  7, CIFAR-10 Batch 5:  0.980808
0.5798
Epoch  8, CIFAR-10 Batch 1:  1.08756
0.573
Epoch  8, CIFAR-10 Batch 2:  1.02043
0.581
Epoch  8, CIFAR-10 Batch 3:  0.96025
0.5808
Epoch  8, CIFAR-10 Batch 4:  0.951963
0.5884
Epoch  8, CIFAR-10 Batch 5:  0.958122
0.5884
Epoch  9, CIFAR-10 Batch 1:  1.00909
0.5826
Epoch  9, CIFAR-10 Batch 2:  0.978864
0.596
Epoch  9, CIFAR-10 Batch 3:  0.896663
0.5944
Epoch  9, CIFAR-10 Batch 4:  0.872807
0.598
Epoch  9, CIFAR-10 Batch 5:  0.875872
0.6032
Epoch 10, CIFAR-10 Batch 1:  0.943985
0.6028
Epoch 10, CIFAR-10 Batch 2:  0.915934
0.6068
Epoch 10, CIFAR-10 Batch 3:  0.846296
0.5988
Epoch 10, CIFAR-10 Batch 4:  0.821391
0.612
Epoch 10, CIFAR-10 Batch 5:  0.850189
0.6002
Epoch 11, CIFAR-10 Batch 1:  0.913553
0.602
Epoch 11, CIFAR-10 Batch 2:  0.873227
0.6138
Epoch 11, CIFAR-10 Batch 3:  0.849553
0.5878
Epoch 11, CIFAR-10 Batch 4:  0.789071
0.6152
Epoch 11, CIFAR-10 Batch 5:  0.782697
0.6206
Epoch 12, CIFAR-10 Batch 1:  0.88925
0.6046
Epoch 12, CIFAR-10 Batch 2:  0.836205
0.623
Epoch 12, CIFAR-10 Batch 3:  0.831855
0.5804
Epoch 12, CIFAR-10 Batch 4:  0.74879
0.6206
Epoch 12, CIFAR-10 Batch 5:  0.762001
0.6296
Epoch 13, CIFAR-10 Batch 1:  0.805714
0.6144
Epoch 13, CIFAR-10 Batch 2:  0.838461
0.6096
Epoch 13, CIFAR-10 Batch 3:  0.780886
0.5968
Epoch 13, CIFAR-10 Batch 4:  0.723775
0.6212
Epoch 13, CIFAR-10 Batch 5:  0.738169
0.6398
Epoch 14, CIFAR-10 Batch 1:  0.806651
0.6132
Epoch 14, CIFAR-10 Batch 2:  0.77004
0.619
Epoch 14, CIFAR-10 Batch 3:  0.693847
0.6186
Epoch 14, CIFAR-10 Batch 4:  0.717333
0.6098
Epoch 14, CIFAR-10 Batch 5:  0.674476
0.6332
Epoch 15, CIFAR-10 Batch 1:  0.753295
0.6238
Epoch 15, CIFAR-10 Batch 2:  0.730211
0.6322
Epoch 15, CIFAR-10 Batch 3:  0.630472
0.6274
Epoch 15, CIFAR-10 Batch 4:  0.687185
0.6422
Epoch 15, CIFAR-10 Batch 5:  0.652016
0.629
Epoch 16, CIFAR-10 Batch 1:  0.732527
0.6148
Epoch 16, CIFAR-10 Batch 2:  0.700618
0.6274
Epoch 16, CIFAR-10 Batch 3:  0.63968
0.6292
Epoch 16, CIFAR-10 Batch 4:  0.709049
0.6266
Epoch 16, CIFAR-10 Batch 5:  0.632986
0.6422
Epoch 17, CIFAR-10 Batch 1:  0.705437
0.6232
Epoch 17, CIFAR-10 Batch 2:  0.694772
0.626
Epoch 17, CIFAR-10 Batch 3:  0.593764
0.6404
Epoch 17, CIFAR-10 Batch 4:  0.618719
0.6376
Epoch 17, CIFAR-10 Batch 5:  0.581663
0.639
Epoch 18, CIFAR-10 Batch 1:  0.630075
0.6362
Epoch 18, CIFAR-10 Batch 2:  0.641854
0.621
Epoch 18, CIFAR-10 Batch 3:  0.561716
0.6348
Epoch 18, CIFAR-10 Batch 4:  0.602747
0.6426
Epoch 18, CIFAR-10 Batch 5:  0.568583
0.647
Epoch 19, CIFAR-10 Batch 1:  0.622813
0.635
Epoch 19, CIFAR-10 Batch 2:  0.633272
0.6092
Epoch 19, CIFAR-10 Batch 3:  0.55119
0.6336
Epoch 19, CIFAR-10 Batch 4:  0.554207
0.6512
Epoch 19, CIFAR-10 Batch 5:  0.532406
0.6388
Epoch 20, CIFAR-10 Batch 1:  0.579411
0.6348
Epoch 20, CIFAR-10 Batch 2:  0.684933
0.5894
Epoch 20, CIFAR-10 Batch 3:  0.573168
0.6224
Epoch 20, CIFAR-10 Batch 4:  0.542699
0.6422
Epoch 20, CIFAR-10 Batch 5:  0.493428
0.6512
Epoch 21, CIFAR-10 Batch 1:  0.532098
0.6464
Epoch 21, CIFAR-10 Batch 2:  0.654738
0.593
Epoch 21, CIFAR-10 Batch 3:  0.571783
0.6102
Epoch 21, CIFAR-10 Batch 4:  0.580984
0.628
Epoch 21, CIFAR-10 Batch 5:  0.464566
0.651
Epoch 22, CIFAR-10 Batch 1:  0.529482
0.6406
Epoch 22, CIFAR-10 Batch 2:  0.594885
0.6108
Epoch 22, CIFAR-10 Batch 3:  0.541695
0.632
Epoch 22, CIFAR-10 Batch 4:  0.522936
0.6516
Epoch 22, CIFAR-10 Batch 5:  0.451552
0.6534
Epoch 23, CIFAR-10 Batch 1:  0.509567
0.6418
Epoch 23, CIFAR-10 Batch 2:  0.555317
0.6384
Epoch 23, CIFAR-10 Batch 3:  0.49823
0.643
Epoch 23, CIFAR-10 Batch 4:  0.476952
0.6538
Epoch 23, CIFAR-10 Batch 5:  0.44645
0.6424
Epoch 24, CIFAR-10 Batch 1:  0.506255
0.6458
Epoch 24, CIFAR-10 Batch 2:  0.568293
0.6322
Epoch 24, CIFAR-10 Batch 3:  0.444706
0.6516
Epoch 24, CIFAR-10 Batch 4:  0.487937
0.6536
Epoch 24, CIFAR-10 Batch 5:  0.430189
0.6498
Epoch 25, CIFAR-10 Batch 1:  0.457535
0.6576
Epoch 25, CIFAR-10 Batch 2:  0.551176
0.6214
Epoch 25, CIFAR-10 Batch 3:  0.438675
0.6418
Epoch 25, CIFAR-10 Batch 4:  0.437272
0.6586
Epoch 25, CIFAR-10 Batch 5:  0.429059
0.6442
Epoch 26, CIFAR-10 Batch 1:  0.431045
0.6526
Epoch 26, CIFAR-10 Batch 2:  0.507526
0.6208
Epoch 26, CIFAR-10 Batch 3:  0.417906
0.6384
Epoch 26, CIFAR-10 Batch 4:  0.436164
0.6536
Epoch 26, CIFAR-10 Batch 5:  0.385224
0.653
Epoch 27, CIFAR-10 Batch 1:  0.399536
0.6536
Epoch 27, CIFAR-10 Batch 2:  0.480617
0.6292
Epoch 27, CIFAR-10 Batch 3:  0.446026
0.6436
Epoch 27, CIFAR-10 Batch 4:  0.417364
0.6584
Epoch 27, CIFAR-10 Batch 5:  0.361315
0.6624
Epoch 28, CIFAR-10 Batch 1:  0.410177
0.6416
Epoch 28, CIFAR-10 Batch 2:  0.491143
0.6252
Epoch 28, CIFAR-10 Batch 3:  0.402241
0.645
Epoch 28, CIFAR-10 Batch 4:  0.419314
0.653
Epoch 28, CIFAR-10 Batch 5:  0.386535
0.65
Epoch 29, CIFAR-10 Batch 1:  0.407958
0.6482
Epoch 29, CIFAR-10 Batch 2:  0.476705
0.6188
Epoch 29, CIFAR-10 Batch 3:  0.366869
0.653
Epoch 29, CIFAR-10 Batch 4:  0.366494
0.6568
Epoch 29, CIFAR-10 Batch 5:  0.349014
0.6612
Epoch 30, CIFAR-10 Batch 1:  0.385157
0.6548
Epoch 30, CIFAR-10 Batch 2:  0.498721
0.6246
Epoch 30, CIFAR-10 Batch 3:  0.353588
0.641
Epoch 30, CIFAR-10 Batch 4:  0.381824
0.6524
Epoch 30, CIFAR-10 Batch 5:  0.325161
0.6652
Epoch 31, CIFAR-10 Batch 1:  0.376686
0.641
Epoch 31, CIFAR-10 Batch 2:  0.417783
0.6502
Epoch 31, CIFAR-10 Batch 3:  0.354053
0.6408
Epoch 31, CIFAR-10 Batch 4:  0.344441
0.656
Epoch 31, CIFAR-10 Batch 5:  0.334344
0.6586
Epoch 32, CIFAR-10 Batch 1:  0.372797
0.6568
Epoch 32, CIFAR-10 Batch 2:  0.422025
0.6454
Epoch 32, CIFAR-10 Batch 3:  0.34145
0.6348
Epoch 32, CIFAR-10 Batch 4:  0.340385
0.6572
Epoch 32, CIFAR-10 Batch 5:  0.280612
0.658
Epoch 33, CIFAR-10 Batch 1:  0.323268
0.6592
Epoch 33, CIFAR-10 Batch 2:  0.379113
0.641
Epoch 33, CIFAR-10 Batch 3:  0.302712
0.6488
Epoch 33, CIFAR-10 Batch 4:  0.348928
0.647
Epoch 33, CIFAR-10 Batch 5:  0.250376
0.6554
Epoch 34, CIFAR-10 Batch 1:  0.344328
0.6512
Epoch 34, CIFAR-10 Batch 2:  0.392583
0.6384
Epoch 34, CIFAR-10 Batch 3:  0.320697
0.6264
Epoch 34, CIFAR-10 Batch 4:  0.313287
0.6422
Epoch 34, CIFAR-10 Batch 5:  0.226866
0.6536
Epoch 35, CIFAR-10 Batch 1:  0.315031
0.6428
Epoch 35, CIFAR-10 Batch 2:  0.38831
0.6274
Epoch 35, CIFAR-10 Batch 3:  0.310194
0.6384
Epoch 35, CIFAR-10 Batch 4:  0.353543
0.6266
Epoch 35, CIFAR-10 Batch 5:  0.270993
0.641
Epoch 36, CIFAR-10 Batch 1:  0.347361
0.6352
Epoch 36, CIFAR-10 Batch 2:  0.456257
0.6092
Epoch 36, CIFAR-10 Batch 3:  0.329135
0.6478
Epoch 36, CIFAR-10 Batch 4:  0.367991
0.6226
Epoch 36, CIFAR-10 Batch 5:  0.286201
0.6496
Epoch 37, CIFAR-10 Batch 1:  0.342068
0.6376
Epoch 37, CIFAR-10 Batch 2:  0.434696
0.6276
Epoch 37, CIFAR-10 Batch 3:  0.275959
0.6546
Epoch 37, CIFAR-10 Batch 4:  0.2948
0.6376
Epoch 37, CIFAR-10 Batch 5:  0.31609
0.628
Epoch 38, CIFAR-10 Batch 1:  0.341899
0.637
Epoch 38, CIFAR-10 Batch 2:  0.387622
0.6364
Epoch 38, CIFAR-10 Batch 3:  0.256899
0.653
Epoch 38, CIFAR-10 Batch 4:  0.297378
0.637
Epoch 38, CIFAR-10 Batch 5:  0.253032
0.6478
Epoch 39, CIFAR-10 Batch 1:  0.323094
0.6328
Epoch 39, CIFAR-10 Batch 2:  0.367586
0.6348
Epoch 39, CIFAR-10 Batch 3:  0.242923
0.655
Epoch 39, CIFAR-10 Batch 4:  0.260086
0.6368
Epoch 39, CIFAR-10 Batch 5:  0.252888
0.6532
Epoch 40, CIFAR-10 Batch 1:  0.246971
0.6458
Epoch 40, CIFAR-10 Batch 2:  0.345772
0.6336
Epoch 40, CIFAR-10 Batch 3:  0.207135
0.6564
Epoch 40, CIFAR-10 Batch 4:  0.231872
0.6496
Epoch 40, CIFAR-10 Batch 5:  0.257861
0.645
Epoch 41, CIFAR-10 Batch 1:  0.248529
0.6424
Epoch 41, CIFAR-10 Batch 2:  0.330545
0.638
Epoch 41, CIFAR-10 Batch 3:  0.17596
0.6632
Epoch 41, CIFAR-10 Batch 4:  0.231253
0.643
Epoch 41, CIFAR-10 Batch 5:  0.289154
0.6352
Epoch 42, CIFAR-10 Batch 1:  0.240611
0.6622
Epoch 42, CIFAR-10 Batch 2:  0.299844
0.6406
Epoch 42, CIFAR-10 Batch 3:  0.218802
0.652
Epoch 42, CIFAR-10 Batch 4:  0.220478
0.6334
Epoch 42, CIFAR-10 Batch 5:  0.297552
0.6284
Epoch 43, CIFAR-10 Batch 1:  0.26076
0.6572
Epoch 43, CIFAR-10 Batch 2:  0.322687
0.6374
Epoch 43, CIFAR-10 Batch 3:  0.202686
0.6526
Epoch 43, CIFAR-10 Batch 4:  0.272402
0.6288
Epoch 43, CIFAR-10 Batch 5:  0.246136
0.6442
Epoch 44, CIFAR-10 Batch 1:  0.423716
0.624
Epoch 44, CIFAR-10 Batch 2:  0.350754
0.63
Epoch 44, CIFAR-10 Batch 3:  0.212171
0.6626
Epoch 44, CIFAR-10 Batch 4:  0.207537
0.643
Epoch 44, CIFAR-10 Batch 5:  0.179021
0.6664
Epoch 45, CIFAR-10 Batch 1:  0.255132
0.6476
Epoch 45, CIFAR-10 Batch 2:  0.300322
0.6366
Epoch 45, CIFAR-10 Batch 3:  0.206684
0.652
Epoch 45, CIFAR-10 Batch 4:  0.188448
0.6406
Epoch 45, CIFAR-10 Batch 5:  0.178477
0.6568
Epoch 46, CIFAR-10 Batch 1:  0.324538
0.619
Epoch 46, CIFAR-10 Batch 2:  0.282484
0.65
Epoch 46, CIFAR-10 Batch 3:  0.184469
0.649
Epoch 46, CIFAR-10 Batch 4:  0.183828
0.6468
Epoch 46, CIFAR-10 Batch 5:  0.191962
0.6498
Epoch 47, CIFAR-10 Batch 1:  0.269841
0.6404
Epoch 47, CIFAR-10 Batch 2:  0.242402
0.6448
Epoch 47, CIFAR-10 Batch 3:  0.159538
0.6554
Epoch 47, CIFAR-10 Batch 4:  0.170903
0.6522
Epoch 47, CIFAR-10 Batch 5:  0.196865
0.6362
Epoch 48, CIFAR-10 Batch 1:  0.217682
0.648
Epoch 48, CIFAR-10 Batch 2:  0.218512
0.648
Epoch 48, CIFAR-10 Batch 3:  0.143181
0.655
Epoch 48, CIFAR-10 Batch 4:  0.14951
0.6556
Epoch 48, CIFAR-10 Batch 5:  0.196995
0.6436
Epoch 49, CIFAR-10 Batch 1:  0.28221
0.6308
Epoch 49, CIFAR-10 Batch 2:  0.264819
0.6274
Epoch 49, CIFAR-10 Batch 3:  0.144478
0.6566
Epoch 49, CIFAR-10 Batch 4:  0.14454
0.6574
Epoch 49, CIFAR-10 Batch 5:  0.178129
0.6324
Epoch 50, CIFAR-10 Batch 1:  0.256226
0.638
Epoch 50, CIFAR-10 Batch 2:  0.225462
0.6468
Epoch 50, CIFAR-10 Batch 3:  0.168518
0.6494
Epoch 50, CIFAR-10 Batch 4:  0.144772
0.647
Epoch 50, CIFAR-10 Batch 5:  0.156248
0.6476

In [19]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import tensorflow as tf
import pickle
import helper
import random

# Set batch size if not already set
try:
    if batch_size:
        pass
except NameError:
    batch_size = 64

save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3

def test_model():
    """
    Test the saved model against the test dataset
    """

    test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
    loaded_graph = tf.Graph()

    with tf.Session(graph=loaded_graph) as sess:
        # Load model
        loader = tf.train.import_meta_graph(save_model_path + '.meta')
        loader.restore(sess, save_model_path)

        # Get Tensors from loaded model
        loaded_x = loaded_graph.get_tensor_by_name('x:0')
        loaded_y = loaded_graph.get_tensor_by_name('y:0')
        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
        
        # Get accuracy in batches for memory limitations
        test_batch_acc_total = 0
        test_batch_count = 0
        
        for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total += sess.run(
                loaded_acc,
                feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1

        print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))

        # Print Random Samples
        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
        random_test_predictions = sess.run(
            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)


test_model()


    

    




Testing Accuracy: 0.6265912234783173