Keras / TF错误：形状不兼容

我有一个错误：

InvalidArgumentError（参见上面的回溯）：不兼容的形状：[12192768]与[4064256] [[Node：mul = Mul [T = DT_FLOAT，_device =“/ job：localhost / replica：0 / task：0 / cpu： 0“]（重塑，重塑_1）]]

这是我的代码：

import numpy as np
import os

from skimage.io import imread, imsave

from keras.models import load_model, Model
from keras.layers import Conv2D, MaxPooling2D, Input, concatenate, Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
from keras import backend as K

K.set_image_dim_ordering('tf')

tbCallBack = TensorBoard(log_dir='./logs',
                         histogram_freq=1,
                         write_graph=True,
                         write_grads=True,
                         write_images=True)


def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) + 1.0)


def dice_coef_loss(y_true, y_pred):
    return -dice_coef(y_true, y_pred)


def build():
    inputs = Input(shape=(1008, 1008, 3))

    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])

    model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
    return model


def prepare_train():
    files = os.listdir('./raws/')
    x_files_names = filter(lambda x: x.endswith('_raw.jpg'), files)
    total = len(x_files_names)

    x_train = np.ndarray((total, 1008, 1008, 3), dtype=np.uint8)
    i = 0
    for x_file_name in x_files_names:
        img = imread(os.path.join('./raws/' + x_file_name))
        x_train[i] = np.array([img])
        i += 1
    np.save('x_train.npy', x_train)

    files = os.listdir('./masks/')
    y_files_names = filter(lambda x: x.endswith('_mask.jpg'), files)
    total = len(y_files_names)

    y_train = np.ndarray((total, 1008, 1008, 3), dtype=np.uint8)
    i = 0
    for y_file_name in y_files_names:
        img = imread(os.path.join('./masks/' + y_file_name))
        y_train[i] = np.array([img])
        i += 1
    np.save('y_train.npy', y_train)


def train():
    x_train = np.load('x_train.npy')
    x_train = x_train.astype('float32')
    x_train /= 255

    y_train = np.load('y_train.npy')
    y_train = y_train.astype('float32')
    y_train /= 255.

    model.fit(x_train,
              y_train,
              batch_size=4,
              epochs=25,
              callbacks=[tbCallBack])
    model.save('model.h5')


def prepare_predict():
    files = os.listdir('./predict_raws/')
    x_files_names = filter(lambda x: x.endswith('_raw.jpg'), files)
    total = len(x_files_names)

    x_train = np.ndarray((total, 1008, 1008, 3), dtype=np.uint8)
    i = 0
    for x_file_name in x_files_names:
        img = imread(os.path.join('./predict_raws/' + x_file_name))
        x_train[i] = np.array([img])
        i += 1
    np.save('x_predict.npy', x_train)


def predict():
    x_predict = np.load('x_predict.npy')
    x_predict = x_predict.astype('float32')
    x_predict /= 255

    predictions = model.predict_on_batch(x_predict)
    np.save('predictions.npy', predictions)


if not os.path.exists('logs'):
    os.makedirs('logs')

if not os.path.exists('raws'):
    os.makedirs('raws')

if not os.path.exists('masks'):
    os.makedirs('masks')

if not os.path.exists('predict_raws'):
    os.makedirs('predict_raws')

if not os.path.exists('predict_masks'):
    os.makedirs('predict_masks')

zero_choice = raw_input('Prepare training data? (y or n): ')
if zero_choice == 'y':
    prepare_train()

frst_choice = raw_input('Please, enter needed action (load or train): ')
if frst_choice == 'load':
    model = load_model('model.h5')
elif frst_choice == 'train':
    model = build()
    train()

scnd_choice = raw_input('Prepare test data? (y or n): ')
if scnd_choice == 'y':
    prepare_predict()

thrd_choice = raw_input('Model is ready! Start prediction? (y or n): ')
if thrd_choice == 'y':
    predict()
elif thrd_choice == 'n':
    exit()

这是错误的全文：

Epoch 1/25
Traceback (most recent call last):
  File "segmenting_network.py", line 162, in <module>
    train()
  File "segmenting_network.py", line 111, in train
    callbacks=[tbCallBack])
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1430, in fit
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1079, in _fit_loop
    outs = f(ins_batch)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 2268, in __call__
    **self.session_kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [12192768] vs. [4064256]
         [[Node: gradients/mul_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@mul"], _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/mul_grad/Shape, gradients/mul_grad/Shape_1)]]

Caused by op u'gradients/mul_grad/BroadcastGradientArgs', defined at:
  File "segmenting_network.py", line 162, in <module>
    train()
  File "segmenting_network.py", line 111, in train
    callbacks=[tbCallBack])
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1413, in fit
    self._make_train_function()
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 937, in _make_train_function
    self.total_loss)
  File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 404, in get_updates
    grads = self.get_gradients(loss, params)
  File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 71, in get_gradients
    grads = K.gradients(loss, params)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 2305, in gradients
    return tf.gradients(loss, variables, colocate_gradients_with_ops=True)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 540, in gradients
    grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 346, in _MaybeCompile
    return grad_fn()  # Exit early
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 540, in <lambda>
    grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 663, in _MulGrad
    rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 395, in _broadcast_gradient_args
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

...which was originally created as op u'mul', defined at:
  File "segmenting_network.py", line 161, in <module>
    model = build()
  File "segmenting_network.py", line 68, in build
    model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 840, in compile
    sample_weight, mask)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 446, in weighted
    score_array = fn(y_true, y_pred)
  File "segmenting_network.py", line 29, in dice_coef_loss
    return -dice_coef(y_true, y_pred)
  File "segmenting_network.py", line 24, in dice_coef
    intersection = K.sum(y_true_f * y_pred_f)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 838, in binary_op_wrapper
    return func(x, y, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1061, in _mul_dispatch
    return gen_math_ops._mul(x, y, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1377, in _mul
    result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Incompatible shapes: [12192768] vs. [4064256]
         [[Node: gradients/mul_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@mul"], _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/mul_grad/Shape, gradients/mul_grad/Shape_1)]]

版本：

Keras 2.0.6

TF 1.2.1

NP 1.13.1

我唯一的想法是减少批量的大小，但它没有帮助 . 有人有什么想法吗？

对于训练我使用11张图像，1008 * 1008大小和3通道颜色 .