次の問題があります。
tensorflow で 3d CNN をトレーニングしようとしています。データをトレーニング、検証、テストの 3 つのデータセットに分けました。
主な問題は、5 エポックのトレーニング後に検証セットをテストすると、モデルの出力が 5 つの画像でほぼ同じになることです。(これは、ソフトマックスのない最後のレイヤーの出力です)
2018-04-17 23:30:35.134318 Prediction: [[0.8185656 2.7571523 ]
[0.8200048 2.7590456 ]
[0.8185656 2.7571523 ]
[0.8200048 2.7590458 ]
[0.7751368 2.7532804 ]
[0.82061136 2.7588618 ]
[0.8130686 2.7821052 ]
[0.83537185 2.7514493 ]
[0.8200041 2.7590454 ]
[0.81701267 2.7519925 ]
[0.8424163 2.8674953 ]
[0.82000506 2.7590454 ]
[0.81999433 2.7590487 ]
[0.81701267 2.7519925 ]
ただし、トレーニング セットに対して同じことを行うと、従来の予測が得られます。
データセットを完全にチェックしましたが、どちらも正しく、同じ条件です。
これは、モデルを構築してトレーニングを行うために使用した私のモードです。
クラス Cnn3DMRI(オブジェクト):
def weight_variable(self, shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv3d(self, x, W):
return tf.nn.conv3d(x, W, strides=[1, 1, 1, 1, 1], padding='SAME')
def maxpool3d(self, x):
# size of window movement of window
return tf.nn.max_pool3d(x, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding='SAME')
def dense_to_one_hot(self, labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = np.arange(num_labels) * num_classes
labels_one_hot = np.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def wrapper_image(self, full_image_set, full_label_set, last_batch=0, batch_size=5):
batch_img = full_image_set[last_batch:batch_size+last_batch, :, :, :]
batch_label = full_label_set[last_batch:batch_size+last_batch]
return batch_img, batch_label, batch_size+last_batch
def convolutional_neural_network(self, x, img_sz, n_slices):
weights = {
'W_conv1': self.weight_variable([3, 5, 5, 1, 32]),
'W_conv2': self.weight_variable([2, 5, 5, 32, 48]),
'W_fc': self.weight_variable(
[
int(
math.ceil(
n_slices / 8
) * math.ceil(
img_sz / 8
) * math.ceil(
img_sz / 8
) *48), 2048
]
),
'W_fc2': self.weight_variable([2048, 1024]),
'out': self.weight_variable([1024, 2])
}
biases = {
'b_conv1': self.bias_variable([32]),
'b_conv2': self.bias_variable([48]),
'b_fc': self.bias_variable([2048]),
'b_fc2': self.bias_variable([1024]),
'out': self.bias_variable([2])
}
self.x_im = tf.reshape(x, shape=[-1, n_slices, img_sz, img_sz, 1])
conv1 = tf.nn.relu(self.conv3d(self.x_im, weights['W_conv1']) + biases['b_conv1'])
conv1 = tf.Print(conv1,[conv1], 'The conv1: ')
conv1 =self.maxpool3d(conv1)
conv1 = tf.Print(conv1,[conv1], 'The max1: ')
conv2 = tf.nn.relu(self.conv3d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv1 = tf.Print(conv2,[conv2], 'The conv2: ')
conv2 = tf.nn.max_pool3d(conv2, ksize=[1, 4, 4, 4, 1], strides=[1, 4, 4, 4, 1],
padding='SAME')
conv2 = tf.Print(conv2,[conv2], 'The max2: ')
fc = tf.reshape(conv2, [-1,int(math.ceil(n_slices/8)*math.ceil(img_sz/8)*math.ceil(
img_sz/8))*48])
fc = tf.Print(fc,[fc], 'The reshape: ')
fc2 = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc2 = tf.Print(fc2,[fc2], 'The fc: ')
dp1 = tf.nn.dropout(fc2, self.keep_prob)
fc3 = tf.nn.relu(tf.matmul(dp1, weights['W_fc2'])+biases['b_fc2'])
fc3 = tf.Print(fc3,[fc3], 'The fc2: ')
dp2 = tf.nn.dropout(fc3, self.keep_prob)
output = tf.matmul(dp2, weights['out'])+biases['out']
output = tf.Print(output,[output], 'The output: ')
return output
def test_validation_set(self, sess, data_validation, label_validation, valid_batch_size=60):
batch_img, batch_label, last_batch = self.wrapper_image(
data_validation, label_validation, self.last_valid_batch, valid_batch_size
)
batch_label = self.dense_to_one_hot(
np.array(batch_label, dtype=np.int),2
).astype(np.float32)
if last_batch+valid_batch_size < len(label_validation):
self.last_valid_batch = last_batch
else:
self.last_valid_batch = 0
pred, c, validation_accuracy = sess.run(
[self.prediction, self.cost, self.accuracy], feed_dict={
self.x: batch_img, self.y_: batch_label, self.keep_prob: 1.0
}
)
self.log("Prediction: "+str(pred))
self.log("Label: "+str(batch_label))
self.log("Validation accuracy: "+str(validation_accuracy))
self.log("Validation cost: "+str(c))
return validation_accuracy, c
def train_neural_network(self, data_img, labels, data_validation, label_validation,
batch_size, img_sz, n_slices, last_batch,
keep_rate, model_path):
self.prediction = self.convolutional_neural_network(self.x, img_sz, n_slices)
self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.y_,
logits=self.prediction))
optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.cost)
correct_prediction = tf.equal(tf.argmax(self.prediction, 1), tf.argmax(self.y_, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
hm_epochs = 1000
saver = tf.train.Saver(tf.trainable_variables())
epoch_loss = 0
epoch_loss_mean = []
n_epoch = 0
learning_rate = 1e-4
self.last_valid_batch = 0
min_valid_cost = 0
all_valid_cost = []
model_path_train = 'model_train/my_model.ckpt'
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if model_path:
pass
#saver.restore(sess, model_path_train)
while n_epoch < hm_epochs:
if len(data_img)>last_batch+batch_size:
with tf.device('/cpu:0'):
#batch_img, batch_label, last_batch = self.get_image(
# data_img, labels, last_batch, batch_size, img_sz, n_slices
#)
batch_img, batch_label, last_batch = self.wrapper_image(data_img, labels, last_batch, batch_size)
print "Batch label images: "+str(batch_label)
batch_label = self.dense_to_one_hot(np.array(batch_label, dtype=np.int),
2).astype(np.float32)
else:
with tf.device('/cpu:0'):
restbatch = last_batch + batch_size - len(data_img)
batch_img = np.concatenate((
self.wrapper_image(data_img, labels, last_batch, len(data_img) -
last_batch)[0],
self.wrapper_image(data_img, labels, last_batch, len(data_img) -
last_batch)[0]
))
batch_label = np.concatenate((
self.wrapper_image(data_img, labels, last_batch, len(data_img) -
last_batch)[1],
self.wrapper_image(data_img, labels, last_batch, len(data_img) -
last_batch)[1]
))
batch_label = self.dense_to_one_hot(np.array(batch_label, dtype=np.int),
2).astype(
np.float32)
last_batch = restbatch
####### at the end of EACH EPOCH ###
epoch_loss_mean.append(epoch_loss)
print "epoch loss mean: "+str(epoch_loss_mean)
epoch_loss = 0
n_epoch += 1
print "n_epoch: "+str(n_epoch)
if model_path:
saver.save(sess, model_path_train)
if not n_epoch % 5:
valid_accuracy, valid_cost = self.test_validation_set(sess,data_validation,
label_validation, 60)
if valid_cost < min_valid_cost - 2:
min_valid_cost = valid_cost
if model_path:
saver.save(sess, model_path)
all_valid_cost.append(valid_cost)
print all_valid_cost
if self.last_valid_batch == 0:
self.shufle_data(data_validation, label_validation)
train_accuracy = self.accuracy.eval(
feed_dict={self.x: batch_img, self.y_: batch_label, self.keep_prob: 1.0})
print "trainning accuracy: " + str(train_accuracy)
self.shufle_data(data_img, labels)
_, c, pred = sess.run(
[optimizer, self.cost,], feed_dict={
self.x: batch_img, self.y_: batch_label, self.keep_prob: keep_rate,
self.learning_rate: learning_rate
}
)
print 'epoch_loss: '+str(c)
def main(self, data_dir, labels_dir, img_sz, n_slices, batch_size=5, last_batch=0, train=False,
model_path=None, keep_rate=0.5):
"""
Args:
data_dir(list): directories of the image to be tested
labels_dir: (str): directory of the csv file where the image are labeled, the index
colum is the number 2 and the labels header is 'Diag'.
img_sz: the spatial image size the be transformed to. that is the sizes with which
the image will be trainned. width and hight must be the same
n_slices: the number of slices for the image to be trained
last_batch: the batch at which you want to start the trainning
train: boolean to set trainning: 0 or testing :1
model_path: the path where the model is saved, if there is no previous model you can
set a path here to start a new one.
keep_rate: the keep_probability of firing a node by means of dropout
Returns:
"""
self.train = train
data_path_trainning, label_trainning, data_path_validation, label_validation, \
data_testing, label_testing = self.load_dataset(data_dir, labels_dir,)
data_trainning, label_trainning_final = self.load_image(data_path_trainning,
label_trainning, img_sz, n_slices
)
data_validation, label_validation_final = self.load_image(
data_path_validation, label_validation, img_sz, n_slices
)
self.x = tf.placeholder(tf.float32, shape=[None, n_slices, img_sz, img_sz]) #batch_size,
# image_Size
self.y_ = tf.placeholder(tf.float32, shape=[None, 3]) #batch_size, label_size
self.learning_rate = tf.placeholder(tf.float32)
self.keep_prob = tf.placeholder(tf.float32)
if train:
self.train_neural_network(data_trainning, label_trainning_final, data_validation,
label_validation_final, batch_size, img_sz, n_slices,
last_batch, keep_rate, model_path
)
私はすでに tf.set_random_seed( 1 ) を試しましたが、修正は見られません
どなたかアイデアをお持ちですか？
本当にありがとう
22/04/18 編集:
分類するデータは、バイクラス問題で 150x150x40 ピクセルの 3D 画像です。各クラスの約半分の合計 400 枚の画像があります。データセットをトレーニング (75%)、検証 (10%)、テスト (15%) に分けました。
Edit2: モデルを少し単純化しました。見上げる
また、クラスが2つしかないことにも言及してください
20 枚の画像のみでモデルをトレーニングした別のチェックを試みました。0 コストが得られるかどうかを確認します。
125 エポック後の結果:
2018-04-24 23:58:24.992421 epoch loss mean: [4549.9554141853, 1854.6537470817566, 817.4076923541704, 686.8368729054928, 687.7348744268759, 704.946801304817, 483.6952783479355, 260.2293045549304, 272.66821688037817, 116.57515235748815, 97.86094704543848, 90.43152131629176, 132.54018089070996, 69.62595339218387, 57.412255316681694, 79.66184640157735, 70.99515068903565, 55.75798599421978 , 44.14403077028692, 38.901107819750905, 49.75594720244408, 52.6321079954505, 37.70595762133598, 42.07099115010351, 29.01994925737381, 28.365123450756073, 31.93120799213648, 43.9855432882905, 33.242121398448944, 36.57513061538339, 28.828659534454346, 29.847569406032562, 24.078316539525986, 31.630925316363573, 30.5430103354156, 26.18060240149498, 32.86780231446028, 25.42889341711998, 29.355055704712868, 26.269534677267075, 24.921810917556286, 27.15281054377556, 27 .343381822109222, 24.293660208582878, 28.212179094552994, 25.07626649737358, 21.650991335511208, 25.7527906447649, 23.42476052045822, 28.350880563259125, 22.57907184958458, 21.601420983672142, 25.28128480911255, 25.550641894340515, 22.444457232952118, 27.660063683986664, 21.863914296031, 25.722180172801018, 24.00674758851528, 21.46472266316414, 26.599679857492447, 23.52132275700569, 26.1786640137434, 24.842691332101822, 25.263965144753456, 22.730938494205475, 22.787407517433167, 23.58866274356842, 25.351682364940643, 23.85272353887558, 23.884423837065697, 24.685379207134247, 22.55106496810913, 25.993630707263947, 21.967322662472725, 22.651918083429337, 21.91003155708313, 23.782021015882492, 21.567724645137787, 22.130879193544388, 21.33636975288391, 25.624440014362335, 23.26347705721855, 22.370914071798325, 22.614411562681198, 24.962509214878082, 22.121410965919495, 20.644148647785187, 24.472172617912292, 21.622991144657135, 21.719978988170624, 21.72349101305008, 21.729621797800064, 22.090826153755188, 21.44688707590103, 22.34817299246788, 22.93226248025894, 22.63547444343567, 22.1306095123291, 22.16277289390564, 22.83771103620529, 24.171751350164413, 22.025538682937622, 21.339059710502625, 22.169043481349945, 24.614955246448517, 22.83159503340721, 21.43451902270317, 21.54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623, 23.04211688041687, 24.00969059765339, 21.8588485121727, 22.199619591236115] 2018-04-24 23:58:24.992694 n_epoch: 125622991144657135, 21.719978988170624, 21.72349101305008, 21.729621797800064, 22.090826153755188, 21.44688707590103, 22.34817299246788, 22.93226248025894, 22.63547444343567, 22.1306095123291, 22.16277289390564, 22.83771103620529, 24.171751350164413, 22.025538682937622, 21.339059710502625, 22.169043481349945, 24.614955246448517, 22.83159503340721, 21.43451902270317, 21.54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623, 23.04211688041687, 24.00969059765339, 21.8588485121727, 22.199619591236115] 2018-04-24 23:58:24.992694 n_epoch: 125622991144657135, 21.719978988170624, 21.72349101305008, 21.729621797800064, 22.090826153755188, 21.44688707590103, 22.34817299246788, 22.93226248025894, 22.63547444343567, 22.1306095123291, 22.16277289390564, 22.83771103620529, 24.171751350164413, 22.025538682937622, 21.339059710502625, 22.169043481349945, 24.614955246448517, 22.83159503340721, 21.43451902270317, 21.54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623, 23.04211688041687, 24.00969059765339, 21.8588485121727, 22.199619591236115] 2018-04-24 23:58:24.992694 n_epoch: 12534817299246788, 22.93226248025894, 22.63547444343567, 22.1306095123291, 22.16277289390564, 22.83771103620529, 24.171751350164413, 22.025538682937622, 21.339059710502625, 22.169043481349945, 24.614955246448517, 22.83159503340721, 21.43451902270317, 21.54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623、23.04211688041687、24.00969059765339、21.8588485121727、22.199619591236115] 2018-04-24 23:58:24.992694 1 n_5エポック34817299246788, 22.93226248025894, 22.63547444343567, 22.1306095123291, 22.16277289390564, 22.83771103620529, 24.171751350164413, 22.025538682937622, 21.339059710502625, 22.169043481349945, 24.614955246448517, 22.83159503340721, 21.43451902270317, 21.54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623、23.04211688041687、24.00969059765339、21.8588485121727、22.199619591236115] 2018-04-24 23:58:24.992694 1 n_5エポック54544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623, 23.04211688041687, 24.00969059765339, 21.8588485121727, 22.199619591236115] 2018-04-24 23:58:24.992694 n_epoch: 12554544973373413, 22.889380514621735, 24.168621599674225, 21.947510302066803, 22.30243694782257, 22.381454586982727, 22.50485634803772, 22.61657750606537, 22.288170099258423, 21.30070123076439, 22.489792048931122, 21.885000944137573, 21.343613982200623, 23.04211688041687, 24.00969059765339, 21.8588485121727, 22.199619591236115] 2018-04-24 23:58:24.992694 n_epoch: 125
各レイヤーの印刷出力:
conv1: [[[[[0.0981627107 0.100793235 0.0934509188]]]]...]
max1: [[[[[0.102978 0.107030481 0.0977560952]]]]...]
max2: [[[[[0 0 0.00116439909]]]]...]
変形: [[0 0 0.00116439909]...]
fc: [[0.01167579 0.182256863 0.107154548]...]
fc2: [[0.773868561 0.364259362 0]...]
出力: [[0.16590938 -0.255491495][0.16590938]...]
conv1: [[[[[0.0981602222 0.100800745 0.0934513509]]]]...]
max1: [[[[[0.102975294 0.107038349 0.0977560282]]]]...]
max2: [[[[[0 0 0.000874094665]]]]...]
変形: [[0 0 0.000874094665]...]
fc: [[0.0117974132 0.182980478 0.106876813]...]
fc2: [[0.774896204 0.36372292 0]...]
出力: [[0.129838273 -0.210624188][0.129838273]...]
60 個のサンプルをオーバーフィットするには 125 エポックで十分ではないでしょうか?
何が起こっているかについて何か考えはありますか？