我是Tensorflow的新手,我嘗試訓練我的CNN模型將在未來對人臉進行分類。我有一個56人的圖像數據集和它們的裁剪面,形狀爲[-1,224,224,3]和float32類型的numpy數組。當我嘗試feed_dict到tensorflow 我只是附上我train_X和train_Y看怎麼樣餵養到tensorflowValueError:形狀爲'(?,224,224,3)'的張量'Placeholder_3:0'無法提供形狀的值(224,224,3)'
我得到的典型錯誤ValueError異常:不能養活形狀的值(224,224,3)對於張量'Placeholder_3:0',其形狀爲'(?,224,224,3)'。這似乎很容易理解,但我不知道如何修改我的代碼,使其工作。
我Tensorflow代碼是在這裏
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
#config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.6
n_classes = 56
batch_size = 1
hm_epochs = 100
#x = tf.placeholder('float', [None, 150528])
x = tf.placeholder('float', [None, 224,224,3])
y = tf.placeholder('float')
keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
# size of window movement of window
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,3,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([224*224*3,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 224, 224, 3])
#x = train_X
#creating the first layer of CNN
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1']) # activation function 1
conv1 = maxpool2d(conv1)
#creating the second layer of CNN
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2']) # activation function 2
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 224*224*3])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
return output
def train_neural_network(x):
i = 0
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
with tf.Session(config = config) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(len(train_X)/batch_size)):
_, c = sess.run([optimizer, cost], feed_dict={x: train_X[i:i+batch_size], y: train_y[i:i+batch_size]}) #HERE IS THE ERROR
epoch_loss += c
i += 100
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)
train_neural_network(x)
,我會非常高興,如果有人能幫助我弄清楚一切了。預先感謝您的幫助。 P.S再見,我需要以我的GPU不會給我的OOM的方式批量處理數據。因爲我可以改變餵養方式來排除配料,並且除了OOM錯誤外,它工作正常。這是一個有趣的故事,當我重新啓動內核並再次運行它時。發生另一個錯誤 - InvalidArgumentError(請參閱上面的回溯):輸入重塑是200704值的張量,但所需的形狀需要150528的倍數。200704根本不能在這裏,因爲這是224 * 224 * 4當我只有224 * 224 * 3
將您的train_X重塑爲[-1,224,224,3],您正在饋送一個輸入,因此它應該是[1,224,224,3]而不是[224,224,3] –