有人可以帮我解决这个输入错误吗？

Question

I am training a 3D CNN for image classification, however I am getting the following error I have the tensorflow as the backend. I keep getting this error when it runs model.fit().

I checked out most of the related problems posted online, but they all kind of focus on whether it's theaon or tensorflow as the backend. Some of them suggests expand dimensions, but still doesn't work and some other problems showed up.

My model

from keras.models import Sequential, Model
from keras.losses import categorical_crossentropy

def get_model_compiled(shapeinput, num_class):
    clf = Sequential()
    clf.add(Conv3D(32, kernel_size=(3, 3, 1), input_shape=shapeinput))
    clf.add(BatchNormalization())
    clf.add(Activation('relu'))
    clf.add(Conv3D(64, (5, 5, 16)))
    clf.add(BatchNormalization())
    clf.add(Activation('relu'))
    clf.add(MaxPooling3D(pool_size=(2, 2, 2)))
    clf.add(GlobalAveragePooling3D())
    clf.add(Dense(64, kernel_regularizer=regularizers.l2(0)))

    clf.add(Dense(num_class, activation='softmax'))
    clf.compile(loss=categorical_crossentropy, optimizer=Adam(lr=0.001), metrics=['accuracy'])
    return clf

import argparse
import numpy as np
import sys
import pickle

from sklearn.metrics import accuracy_score
sys.path.insert(0, "lib")

import h5py

f=h5py.File('IP28-28-27.h5','r')
train_images=f['data'][:]
train_labels=f['label'][:]
f.close()

train_labels = np.argmax(train_labels,1)

indices = np.arange(train_images.shape[0])
shuffled_indices = np.random.permutation(indices)
images = train_images[shuffled_indices]
labels = train_labels[shuffled_indices]

X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.8, 
random_state=345)

n_classes = labels.max() + 1
i_labeled = [] 
for c in range(n_classes):
    i = indices[labels==c][:5]##change sample number
    i_labeled += list(i)
X_train = images[i_labeled]
X_train = X_train.reshape(-1,27,28,28)
y_train = labels[i_labeled]
X_test = images[i_labeled]
X_test = X_train.reshape(-1,27,28,28)
y_test = labels[i_labeled]


filepath = "best-model_ip.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

import time
import datetime
import collections
       
inputshape = X_train.shape
clf = get_model_compiled(inputshape, num_class=16)
history = clf.fit(x=X_train, y=y_train, batch_size=32, epochs=50, callbacks=callbacks_list)

The error I am getting:

---

 ValueError                                Traceback (most recent call last)
 <ipython-input-36-a7e7b3215008> in <module>
 59 inputshape = X_train.shape
 60 clf = get_model_compiled(inputshape, num_class=16)
 61 history = clf.fit(x=X_train, y=y_train, batch_size=32, epochs=50, callbacks=callbacks_list)
 62 toc1 = time.clock()
 63 print(' Training Time: ', toc1 - tic1)

~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, 
epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, 
sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
950             sample_weight=sample_weight,
951             class_weight=class_weight,
952             batch_size=batch_size)
953         # Prepare validation data.
954         do_validation = False

~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py in _standardize_user_data(self, 
x, y, sample_weight, class_weight, check_array_lengths, batch_size)
749             feed_input_shapes,
750             check_batch_axis=False,  # Don't enforce the batch size.
751             exception_prefix='input')
752 
753         if y is not None:

~/anaconda3/lib/python3.7/site-packages/keras/engine/training_utils.py in 
standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
126                         ': expected ' + names[i] + ' to have ' +
127                         str(len(shape)) + ' dimensions, but got array 
128                         'with shape ' + str(data_shape))
129                 if not check_batch_axis:
130                     data_shape = data_shape[1:]

ValueError: Error when checking input: expected conv3d_15_input to have 5 dimensions, but got 
array with shape (80, 27, 28, 28)

Answer 1

Base on these lines,

X_train = X_train.reshape(-1,27,28,28)
X_test = X_train.reshape(-1,27,28,28)

it looks like OP is using 3D volumes, where each volume has the shape (27, 28, 28) . It seems to be missing the channel axis. The solution is to add a new dimension for the single channel.

X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]