split = 1
test_fname = 'test' + str(split) + '.txt'
part_dat = False
df_test = create_df(os.path.join(datapath, test_fname),
img_path,
partial_dataset=part_dat,
seed=123)
#%% Create TF dataloader
IMSIZE = (224, 224, 3)
BATCH_SIZE = 32
test_ds = create_tf_dataset(df_test, imsize=IMSIZE, onehot=True)
test_ds = test_ds.batch(BATCH_SIZE)
##
#%% Evaluation
modelpth = 'D:\\Users\\Mikko Impiö\\kandi\\models'
from tensorflow.keras.models import load_model
model = load_model(os.path.join(modelpth, '09-02-2020_cont_colab.h5'))
preds = model.predict(test_ds, verbose=True)
yhat = np.argmax(preds, axis=1) + 1
y_test = df_test['label']
df_val = create_df(os.path.join(datapath, val_fname),
img_path,
partial_dataset=part_dat,
seed=123)
from sklearn.utils import shuffle
df_train = shuffle(df_train)
df_val = shuffle(df_val)
#%% Create TF dataloader
AUTOTUNE = tf.data.experimental.AUTOTUNE
IMSIZE = (224, 224, 3)
BATCH_SIZE = 8
train_ds = create_tf_dataset(df_train, imsize=IMSIZE, onehot=True)
val_ds = create_tf_dataset(df_val, imsize=IMSIZE, onehot=True)
train_ds = prepare_for_training(train_ds,
shuffle_buffer_size=1000,
batch_size=BATCH_SIZE)
val_ds = prepare_for_training(val_ds,
shuffle_buffer_size=1000,
batch_size=BATCH_SIZE)
for image, label in train_ds.take(5):
print(image.shape)
print(label.shape)
df_test = create_df(os.path.join(datapath, test_fname),
img_path,
partial_dataset=part_dat,
seed=123)
df_val = create_df(os.path.join(datapath, val_fname),
img_path,
partial_dataset=part_dat,
seed=123)
#%% Create TF dataloader
AUTOTUNE = tf.data.experimental.AUTOTUNE
IMSIZE = (224, 224, 3)
BATCH_SIZE = 8
train_ds = create_tf_dataset(df_train, imsize=IMSIZE, onehot=False)
test_ds = create_tf_dataset(df_test, imsize=IMSIZE, onehot=False)
val_ds = create_tf_dataset(df_val, imsize=IMSIZE, onehot=False)
##
train_ds = prepare_for_training(train_ds,
shuffle_buffer_size=len(df_train),
batch_size=BATCH_SIZE)
val_ds = prepare_for_training(val_ds,
shuffle_buffer_size=len(df_val),
batch_size=BATCH_SIZE)
for image, label in train_ds.take(5):
print(image.shape)