ValueError：检查目标时出错：预期i3d的形状为（3，），但数组的形状为（1，）

Question

我正在训练7个类的i3d顶级模型，并遇到以下错误：

  

ValueError：检查目标时出错：预期density_3的形状为（3，），但数组的形状为（1，）

import os
import glob
import time
import sys

import numpy as np
import pandas as pd

import keras
from keras import backend as K

from datagenerator import VideoClasses, FramesGenerator
from model_i3d import Inception_Inflated3d, add_i3d_top


def layers_freeze(keModel:keras.Model) -> keras.Model:

    print("Freeze all %d layers in Model %s" % (len(keModel.layers), keModel.name))
    for layer in keModel.layers:
        layer.trainable = False

    return keModel

def layers_unfreeze(keModel:keras.Model) -> keras.Model:

    print("Unfreeze all %d layers in Model %s" % (len(keModel.layers), keModel.name))
    for layer in keModel.layers:
        layer.trainable = True

    return keModel


def count_params(keModel:keras.Model):

    trainable_count = int(
        np.sum([K.count_params(p) for p in set(keModel.trainable_weights)]))
    non_trainable_count = int(
        np.sum([K.count_params(p) for p in set(keModel.non_trainable_weights)]))

    print('Total params: {:,}'.format(trainable_count + non_trainable_count))
    print('Trainable params: {:,}'.format(trainable_count))
    print('Non-trainable params: {:,}'.format(non_trainable_count))

    return



def train_I3D_oflow_end2end(diVideoSet):
    """ 
    * Loads pretrained I3D model, 
    * reads optical flow data generated from training videos,
    * adjusts top-layers adequately for video data,
    * trains only news top-layers,
    * then fine-tunes entire neural network,
    * saves logs and models to disc.
    """

    # directories
    sFolder = "%03d-%d"%(diVideoSet["nClasses"], diVideoSet["nFramesNorm"])
    sClassFile       = "data-set/%s/%03d/class1.csv"%(diVideoSet["sName"], diVideoSet["nClasses"])
    #sVideoDir        = "data-set/%s/%03d"%(diVideoSet["sName"], diVideoSet["nClasses"])
    #sImageDir        = "data-temp/%s/%s/image"%(diVideoSet["sName"], sFolder)
    #sImageFeatureDir = "data-temp/%s/%s/image-i3d"%(diVideoSet["sName"], sFolder)
    sOflowDir        = "data-temp/%s/%s/oflow"%(diVideoSet["sName"], sFolder)
    #sOflowFeatureDir = "data-temp/%s/%s/oflow-i3d"%(diVideoSet["sName"], sFolder)

    sModelDir        = "model"

    diTrainTop = {
        "fLearn" : 1e-3,
        "nEpochs" : 3}

    diTrainAll = {
        "fLearn" : 1e-4,
        "nEpochs" : 17}

    nBatchSize = 4

    print("\nStarting I3D end2end training ...")
    print(os.getcwd())

    # read the ChaLearn classes
    oClasses = VideoClasses(sClassFile)

    # Load training data
    genFramesTrain = FramesGenerator(sOflowDir + "/train", nBatchSize, 
        diVideoSet["nFramesNorm"], 224, 224, 2, oClasses.liClasses)
    genFramesVal = FramesGenerator(sOflowDir + "/val", nBatchSize, 
        diVideoSet["nFramesNorm"], 224, 224, 2, oClasses.liClasses)

    # Load pretrained i3d model and adjust top layer 
    print("Load pretrained I3D flow model ...")
    keI3DOflow = Inception_Inflated3d(
        include_top=False,
        weights='flow_imagenet_and_kinetics',
        input_shape=(diVideoSet["nFramesNorm"], 224, 224, 2))
    print("Add top layers with %d output classes ..." % oClasses.nClasses)
    keI3DOflow = layers_freeze(keI3DOflow)
    keI3DOflow = add_i3d_top(keI3DOflow, oClasses.nClasses, dropout_prob=0.3)

    # Prep logging
    sLog = time.strftime("%Y%m%d-%H%M", time.gmtime()) + \
        "-%s%03d-oflow-i3d"%(diVideoSet["sName"], diVideoSet["nClasses"])

    # Helper: Save results
    csv_logger = keras.callbacks.CSVLogger("acc.csv", append = True)

    # Helper: Save the model
    os.makedirs(sModelDir, exist_ok=True)
    cpTopLast = keras.callbacks.ModelCheckpoint(filepath = sModelDir + "/" + sLog + "-above-last.h5", verbose = 0)
    cpTopBest = keras.callbacks.ModelCheckpoint(filepath = sModelDir + "/" + sLog + "-above-best.h5",
        verbose = 1, save_best_only = True)
    cpAllLast = keras.callbacks.ModelCheckpoint(filepath = sModelDir + "/" + sLog + "-entire-last.h5", verbose = 0)
    cpAllBest = keras.callbacks.ModelCheckpoint(filepath = sModelDir + "/" + sLog + "-entire-best.h5",
        verbose = 1, save_best_only = True)

    # Fit top layers
    #print("Fit I3D top layers with generator: %s" % (diTrainTop))
    #optimizer = keras.optimizers.Adam(lr = diTrainTop["fLearn"])
    #keI3DOflow.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    #count_params(keI3DOflow)



    # Fit entire I3D model
    print("Finetune all I3D layers with generator: %s" % (diTrainAll))
    keI3DOflow = layers_unfreeze(keI3DOflow)
    optimizer = keras.optimizers.Adam(lr = diTrainAll["fLearn"])
    keI3DOflow.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    count_params(keI3DOflow)    

    keI3DOflow.fit_generator(
        generator = genFramesTrain,
        validation_data = genFramesVal,
        epochs = diTrainAll["nEpochs"],
        workers = 4,                 
        use_multiprocessing = False,
        max_queue_size = 8, 
        verbose = 1,
        callbacks=[csv_logger, cpAllLast, cpAllBest])

    return