卷积神经网络在做图像分类的时候不收敛

Question

I try to train a CNN model, 2 classes, which is based on tensorflow to do the image classification.

I have tried much modification about epochs, learning rate, batch size and the CNN size, but nothing works.

about data

86(label: 0) + 63(label: 1) images

shape: (128, 128)

about current parameters

learning_rate = 0.00005(I have tried from 0.00000001 to 0.8...)

batch size = 30(I also have tried from 5 to 130)

epoch = 20

about network

def weight_variable(shape):

    initial = tf.truncated_normal(shape, stddev = 0.1, dtype = tf.float32)
    return tf.Variable(initial)

def bias_variable(shape):

    initial = tf.constant(0.1, shape = shape, dtype = tf.float32)
    return tf.Variable(initial)

def conv2d(x, W):

    #(input, filter, strides, padding)
    #[batch, height, width, in_channels]
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):

    #(value, ksize, strides, padding)
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def cnn_model():

    epochs = 20
    batch_size = 30
    learning_rate = 0.00005
    hidden = 2
    cap_c = 86
    cap_h = 63
    num = cap_c + cap_h
    image_size = 128
    label_size = 2
 
    print ((num//(batch_size)) * epochs)
    train_loss = np.empty((num//(batch_size)) * epochs)
    train_acc = np.empty((num//(batch_size)) * epochs)

    x = tf.placeholder(tf.float32, shape = [None, image_size, image_size])
    y = tf.placeholder(tf.float32, shape = [None, label_size])

    weight_balance = tf.constant([0.1])

    X_train_ = tf.reshape(x, [-1, image_size, image_size, 1])

    #First layer
    W_conv1 = weight_variable([5, 5, 1, 4])
    b_conv1 = bias_variable([4])
  
    h_conv1 = tf.nn.relu(conv2d(X_train_, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

#    #Second layer
#    W_conv2 = weight_variable([5, 5, 4, 8])
#    b_conv2 = bias_variable([8])
#    
#    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
#    h_pool2 = max_pool_2x2(h_conv2)
#    
#    Third layer
#    W_conv3 = weight_variable([5, 5, 8, 16])
#    b_conv3 = bias_variable([16])
#    
#    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
#    h_pool3 = max_pool_2x2(h_conv3)

    #Full connect layer
    W_fc1 = weight_variable([64 * 64 * 4, hidden])
    b_fc1 = bias_variable([hidden])

    h_pool2_flat = tf.reshape(h_pool1, [-1, 64 * 64 * 4])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

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

    #Output_Softmax

    W_fc2 = weight_variable([hidden, label_size])
    b_fc2 = bias_variable([label_size])

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

    print y_conv.shape

    #Train
    loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(y, y_conv, weight_balance))
    optimize = tf.train.AdamOptimizer(learning_rate).minimize(loss)

    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

about result

The loss is not convergent and also the accuracy.

I don't know if my CNN model is not suitable for my data?
or

The Activate function and loss function of the network is not suitable?

Really thank you