我有25000张彩色图片100 * 100(* 3)的数据集,我正在尝试构建一个带有一个卷积层的简单神经网络。它显示了受疟疾感染或未被疟疾感染的细胞的图片,因此我的输出为2。但是对于每一批,我得到0%的准确性。我的批次大小为1,但我尝试使用其他大小,但我仍然获得0%的准确性。
我的CNN:
def simple_nn(X_training, Y_training, X_test, Y_test):
input = 100*100*3
h1 = 100
batch_size = 1
learning_rate = 0.000001
dropout = 0.2
X = tf.placeholder(tf.float32, [batch_size, 100, 100, 3], name="is_train")
Y_ = tf.placeholder(tf.float32, [None, 2])
#Layers
conv1 = tf.layers.conv2d(X, filters=64, kernel_size=4,
strides=2, padding='SAME',
activation=tf.nn.relu, name="conv1")
conv1 = tf.layers.batch_normalization(conv1)
conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
conv2 = tf.layers.conv2d(conv1, filters=128, kernel_size=3,
strides=2, padding='SAME',
activation=tf.nn.relu, name="conv2")
conv2 = tf.layers.dropout(conv2, rate=dropout)
conv3 = tf.layers.conv2d(conv2, filters=256, kernel_size=3,
strides=2, padding='SAME',
activation=tf.nn.relu, name="conv3")
conv3 = tf.layers.dropout(conv3, rate=dropout)
conv4 = tf.layers.conv2d(conv3, filters=64, kernel_size=3,
strides=2, padding='SAME',
activation=tf.nn.relu, name="conv4")
conv4 = tf.layers.max_pooling2d(conv4, 2, 2)
conv5 = tf.layers.conv2d(conv4, filters=32, kernel_size=3,
strides=2, padding='SAME',
activation=tf.nn.relu, name="conv5")
Y = tf.reshape(conv5, [batch_size,-1])
logits = tf.layers.dense(Y, units=2, activation=tf.nn.relu)
# loss function
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y_, logits=logits)
loss = tf.reduce_mean(tf.cast(cross_entropy, tf.float32))
# % of correct answers found in batch
is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_step = optimizer.minimize(cross_entropy)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(math.floor(len(X_training)/batch_size)):
st = batch_size * i
end = st + batch_size
if end >= math.floor(len(X_training)) - batch_size:
break
batch_X, batch_Y = X_training[st:end], Y_training[st:end]
train_data={X: batch_X, Y_: batch_Y}
sess.run(train_step, feed_dict=train_data)
#Get the accuracy and loss
a, l = sess.run([accuracy, cross_entropy], feed_dict=train_data)
print("acc : "+str(a)+" , loss : "+str(l))
我的输出:
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.69436306]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931662]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6925567]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.69259375]
acc : 0.0 , loss : [0.6912933]
acc : 0.0 , loss : [0.6957785]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6990725]
acc : 0.0 , loss : [0.69037354]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6991633]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.6931472]
acc : 0.0 , loss : [0.700589]
acc : 0.0 , loss : [0.6931472]
我在一个简单的非卷积层上获得了65%的收益(意思是acc=0.65
),但是由于我改用conv,所以acc=0.0
。首先,由于某种原因,我通过使用卷积层时在变量loss
中返回了精度,但是现在我不这么认为,我认为损失函数有问题。
即使我将模型简化为一层,也发生了同样的事情,而我的loss
仍然在0.69
周围。
答案 0 :(得分:1)
您应该最小化缩小的向量。更改此行
x = 0
newValues = []
for value in my_list:
x = x + 1
newValues.append(myCoolFunction(value))
print(x)
对此:
train_step = optimizer.minimize(cross_entropy)
此外,准确性计算中不包括train_step = optimizer.minimize(loss)
层。这样做:
logits
此外,您还将两次激活应用于is_correct = tf.equal(tf.argmax(logits,1), tf.argmax(Y_,1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
层。首先,您拥有logits
,然后使用tf.nn.relu
(与softmax
一起使用)。不确定您是否故意这样做。