使用Keras的密集錯誤

我得到一個錯誤，這在Keras初學者中似乎是一個常見問題。我試圖將彩色圖像分類爲'something'或'not_something'並獲得基本模型，以便我可以調整超參數以更好地瞭解它們的功能。使用Keras的密集錯誤

如果有人能解釋爲什麼我特意在model.fit中獲得我的錯誤，然後解釋我應該關注的一些意義/在手前查找一般意義上的維度（在火車和測試集中）。我不確定密集（單位）在二進制分類符爲2的情況下是否應該爲1，您是否也可以解釋這一點？

錯誤：

```

ValueError: Error when checking target: expected dense_18 to have 4 dimensions, but got array with shape (584, 1)

```

代碼：

```

from identify_mounds import * 
from PIL import Image 
import numpy as np 
np.random.seed(6) 
import os 
import subprocess 
from collections import defaultdict 
import pickle 
from scipy.misc import imread 
from sklearn.model_selection import train_test_split 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Activation, Flatten 
from keras.layers import Convolution2D, MaxPooling2D 
from keras.utils import np_utils 


def train_nomound_mound(dic): 
    X = [] 
    y = [] 
    X_ = [] 

    for im in dic: 
     X.extend(dic[im]['img_lst']) 
     y.extend(dic[im]['label']) 

    for im in X: 
     arr = imread(im) 
     X_.append(arr) 

    X_ = (np.array(X_).reshape(779, 4, 16, 16)/255).astype('float32') 
    y = np.array(y).astype('float32') 

    X_train, X_test, y_train, y_test = train_test_split(X_, y, stratify = y) 

    #Demensions: X_train: (584, 4, 16, 16), y_train: (584,), X_test: (195, 4, 16, 16), y_test: (195,) 

    model = Sequential() 

    batch_size = 128 
    nb_epoch = 12 

    nb_filters = 32 
    kernel_size = (3, 3) 
    input_shape = (4, 16, 16) 
    pool_size = (2, 2) 

    model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], activation='relu', input_shape=input_shape)) 
    model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], activation='relu')) 
    # model.add(MaxPooling2D(pool_size=pool_size)) 
    model.add(Dropout(0.25)) 


    model.add(Dense(32, activation='relu')) 
    model.add(Dropout(.50)) 
    model.add(Dense(1, activation='relu')) 

    model.compile(loss = 'categorical_crossentropy', optimizer='Adadelta', metrics=['accuracy']) 

    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, y_test)) 
    score = model.evaluate(X_test, y_test, verbose=0) 
    print('Test score:', score[0]) 
    print('Test accuracy:', score[1])

```

來源

2017-06-13 eeskonivich

首先，您在Conv2D和Dense圖層之間缺少[Flatten]（https://keras.io/layers/core/#flatten）圖層。使用'model.summary（）'是一種有用的方式來跟蹤張量在網絡中傳播時的形狀。 – dhinckley

更改最後一個緻密層的形狀。它的單位數量應等於班級數量，以便它可以返回softmax輸出 – Nain

謝謝！也意識到我出於某種原因有兩個卷積層。 – eeskonivich

使用binary_crossentropy損失時，具有單個神經元的密集層是合適的。損失需要一個熱點編碼和相同數量的神經元作爲最終輸出層中的類別。從Keras loss documentation：經由to_categorical功能

Note: when using the categorical_crossentropy loss, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros expect for a 1 at the index corresponding to the class of the sample).

一熱編碼在Keras MNIST MLP example簡明地示出。

來源

2017-06-13 18:12:58 dhinckley

使用Keras的密集錯誤

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