Tensorflow 3-Kanal, um der Farbe Eingänge

Ich bin mit tensor-flow-Prozess Farbbilder mit einer convolutional neural network. Ein code-snippet unten.

Mein code läuft so, ich glaube, ich habe die Anzahl der Kanäle rechts. Meine Frage ist, wie kann ich mich richtig, um die rgb-Daten? Ist es in der form rgbrgbrgb oder wäre es rrrgggbbb? Derzeit bin ich mit der letzteren. Danke. Jede Hilfe würde geschätzt werden.

    c_output = 2
    c_input = 784 * 3

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

    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')

    self.c_x = tf.placeholder(tf.float32, shape=[None, c_input])
    self.c_y_ = tf.placeholder(tf.float32, shape=[None, c_output])

    self.W_conv1 = weight_variable([5, 5, 3, 32])
    self.b_conv1 = bias_variable([32])
    self.x_image = tf.reshape(self.c_x, [-1, 28, 28  , 3])
    self.h_conv1 = tf.nn.relu(conv2d(self.x_image, self.W_conv1) + self.b_conv1)
    self.h_pool1 = max_pool_2x2(self.h_conv1)

    self.W_conv2 = weight_variable([5, 5, 32, 64])
    self.b_conv2 = bias_variable([64])

    self.h_conv2 = tf.nn.relu(conv2d(self.h_pool1, self.W_conv2) + self.b_conv2)
    self.h_pool2 = max_pool_2x2(self.h_conv2)

    self.W_fc1 = weight_variable([7 * 7 * 64, 1024])
    self.b_fc1 = bias_variable([1024])

    self.h_pool2_flat = tf.reshape(self.h_pool2, [-1, 7 * 7 * 64 ])
    self.h_fc1 = tf.nn.relu(tf.matmul(self.h_pool2_flat, self.W_fc1) + self.b_fc1)

    self.keep_prob = tf.placeholder(tf.float32)
    self.h_fc1_drop = tf.nn.dropout(self.h_fc1, self.keep_prob)

    self.W_fc2 = weight_variable([1024, c_output])
    self.b_fc2 = bias_variable([c_output])

    self.y_conv = tf.matmul(self.h_fc1_drop, self.W_fc2) + self.b_fc2

    self.c_cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(self.y_conv, self.c_y_))
    self.c_train_step = tf.train.AdamOptimizer(1e-4).minimize(self.c_cross_entropy)
    self.c_correct_prediction = tf.equal(tf.argmax(self.y_conv, 1), tf.argmax(self.c_y_, 1))
    self.c_accuracy = tf.reduce_mean(tf.cast(self.c_correct_prediction, tf.float32))