Formkonflikt zwischen Logits und Labels

Question

Ich füttere meine Sensoren mit Bildern, die Formen 160 * 60 * 1 haben.

Meine CNN Struktur ist wie folgt

X_input = tf.placeholder(tf.float32, [None, 160 * 60]) 
X = tf.reshape(X_input, shape=[-1, 160, 60, 1]) 
Y_ = tf.placeholder(tf.float32, [None, 5 * image_reader.NUM_CLASSES]) 

learning_rate = 0.001 


def create_fully_connected_weight(shape): 
    return tf.Variable(tf.truncated_normal(shape, stddev=0.1)) 


def create_conv_weight(patch_height, patch_width, input_channel, output_channel): 
    initial = tf.truncated_normal(shape=[patch_height, patch_width, input_channel, output_channel], stddev=0.1) 
    return tf.Variable(initial) 


def create_bias(shape): 
    initial = 0.1 * tf.random_normal(shape=shape) 
    return tf.Variable(initial) 


def create_strides(batch_step, height_step, width_step, channel_step): 
    return [batch_step, height_step, width_step, channel_step] 


def create_conv_layer(input, W, strides, padding='SAME'): 
    return tf.nn.conv2d(input, W, strides, padding) 


def apply_max_pool(x, ksize, strides, padding='SAME'): 
    return tf.nn.max_pool(x, ksize, strides, padding) 


keep_prob = tf.placeholder(tf.float32) 

W1 = create_conv_weight(5, 5, 1, 32) 
print("W1 shape:", W1.get_shape()) 
B1 = create_bias([32]) 
strides1 = create_strides(1, 1, 1, 1) 
Y1 = tf.nn.relu(create_conv_layer(X, W1, strides1, padding="SAME") + B1) 
Y1 = apply_max_pool(Y1, [1, 2, 2, 1], [1, 2, 2, 1]) 
Y1 = tf.nn.dropout(Y1, keep_prob=keep_prob) 
print(Y1) 

W2 = create_conv_weight(5, 5, 32, 64) 
print("W2 shape:", W2.get_shape()) 
B2 = create_bias([64]) 
strides2 = create_strides(1, 1, 1, 1) 
Y2 = tf.nn.relu(create_conv_layer(Y1, W2, strides2, padding="SAME") + B2) 
Y2 = apply_max_pool(Y2, [1, 2, 2, 1], [1, 2, 2, 1]) 
Y2 = tf.nn.dropout(Y2, keep_prob=keep_prob) 
print(Y2) 

W3 = create_conv_weight(5, 5, 64, 128) 
print("W3 shape:", W3.get_shape()) 
B3 = create_bias([128]) 
strides3 = create_strides(1, 1, 1, 1) 
Y3 = tf.nn.relu(create_conv_layer(Y2, W3, strides3, padding="SAME") + B3) 
Y3 = apply_max_pool(Y3, [1, 2, 2, 1], [1, 2, 2, 1]) 
Y3 = tf.nn.dropout(Y3, keep_prob=keep_prob) 
print(Y3) 

# keep_prob = tf.placeholder(tf.float32) 

Y3 = tf.reshape(Y3, [-1, 20 * 10 * 128]) 

W4 = create_fully_connected_weight([20 * 10 * 128, 1024]) 
print("W4 shape:", W4.get_shape()) 
B4 = create_bias([1024]) 
Y4 = tf.nn.relu(tf.matmul(Y3, W4) + B4) 
Y4 = tf.nn.dropout(Y4, keep_prob=keep_prob) 
print(Y4) 

W5 = create_fully_connected_weight([1024, 5 * image_reader.NUM_CLASSES]) 
print("W5 shape:", W5.get_shape()) 
B5 = create_bias([5 * image_reader.NUM_CLASSES]) 
Ylogits = tf.matmul(Y4, W5) + B5 

Doch während ich den Verlust

cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=Ylogits, labels=Y_) 
loss = tf.reduce_mean(cross_entropy) 

ich einen Fehler nehme zu berechnen versuchen die sagt Inkompatible Formen: [200,55] vs . [250, 55] Ich glaube, ich habe ein Problem, das mit Layern zusammenhängt, aber ich konnte es stundenlang nicht finden.

Answer 1

wenn 'SAME': output_shape [i] = ceil (input_spatial_shape [i]/Fortschritte [i])

Y1 - 160 ,60 
Y1 - 80, 30 <= 160/2, 60/2 
Y2 - 80, 30 
Y2 - 40, 15 <= 80/2, 30/2 
Y3 - 40, 15 
Y3 - 20, 8 

Ich bin verwirrt, wie Sie vor dem vollständig verbundenen Schichten mit 20,10 werden enden.

Auch der Fehler, den Sie bekommen, sieht aus wie falsch batch_size (250 vs 200)?

Würden Sie bitte ein ausführbares Skript erstellen, das diesen Fehler enthält, damit andere helfen können.