Esempio n. 1
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    '/home/nicoli/github/alexnet/dataset/csv/imdb_csv/imdb_age_regression.csv',
    index_col=0)

# In[3]:

cols = list(df.columns[1:])
in_format = list(df.columns)

# In[9]:

train_dataset = BatchGenerator(box_output_format=cols)
validation_dataset = BatchGenerator(box_output_format=cols)

train_dataset.parse_csv(
    labels_filename=
    '/home/nicoli/github/alexnet/dataset/csv/imdb_csv/imdb_age_regression_train_split_47950-70-10-20.csv',
    images_dir='/home/nicoli/github/alexnet/dataset/imdb-hand-crop/',
    input_format=in_format)

validation_dataset.parse_csv(
    labels_filename=
    '/home/nicoli/github/alexnet/dataset/csv/imdb_csv/imdb_age_regression_val_split_47950-70-10-20.csv',
    images_dir='/home/nicoli/github/alexnet/dataset/imdb-hand-crop/',
    input_format=in_format)

# In[10]:

img_height, img_width, img_depth = (224, 224, 3)

epochs = 100
Esempio n. 2
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in_format = list(df.columns)
cols, in_format


# In[6]:


train_dataset = BatchGenerator(box_output_format=cols)
validation_dataset = BatchGenerator(box_output_format=cols)

train_dataset. parse_csv(labels_filename='dataset/csv/imdb_csv/imdb_age_regression_train_split_47950-70-10-20.csv', 
                        images_dir='dataset/imdb-hand-crop',
                        input_format=in_format)

validation_dataset.parse_csv(labels_filename='dataset/csv/imdb_csv/imdb_age_regression_val_split_47950-70-10-20.csv', 
                             images_dir='dataset/imdb-hand-crop',
                             input_format=in_format)


# In[7]:


img_height, img_width, img_depth = (224,224,3)

epochs = 1000

train_batch_size = 64
shuffle = True
ssd_train = False

validation_batch_size = 32
Esempio n. 3
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# In[2]:

activations = ['relu', 'lrelu']
img_treats = ['image-treat-1', 'image-treat-2', 'image-treat-3']
nets = ['lenet', 'alexnet']

for img_treat in img_treats:
    for net in nets:
        for activation in activations:

            # In[3]:

            test_dataset = BatchGenerator(box_output_format=['class_id'])
            test_dataset.parse_csv(
                labels_filename=
                '/home/nicoli/github/alexnet/dataset/csv/imdb_csv/imdb_age_regression_test_split_47950-70-10-20.csv',
                images_dir=
                '/home/nicoli/github/alexnet/dataset/imdb-hand-crop/',
                input_format=['image_name', 'class_id'])

            # In[4]:

            print("Number of images in the dataset:",
                  test_dataset.get_n_samples())

            # In[5]:

            img_height, img_width, img_depth = (224, 224, 3)

            #epochs = 90

            batch_size = 100