Question

我正在编写使用此处给出的数据集（https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format）来预测手写字符的模型

编辑：（进行注释中建议的更改后）

我现在得到的错误是：ValueError: Error when checking input: expected conv2d_4_input to have shape (28, 28, 1) but got array with shape (249542, 784, 1)

在CNN的代码下面找到：

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import backend as K
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd 

seed = 785
np.random.seed(seed)

dataset = np.loadtxt('../input/A_Z Handwritten Data/A_Z Handwritten Data.csv', delimiter=',')

print(dataset.shape) # (372451, 785)

X = dataset[:,1:785]
Y = dataset[:,0]

(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, test_size=0.33, random_state=seed)

X_train = X_train / 255
X_test = X_test / 255

X_train = X_train.reshape((-1, X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((-1, X_test.shape[0], X_test.shape[1], 1))

print(X_train.shape) # (1, 249542, 784, 1)

Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)

print(Y_test.shape) # (122909, 26)

num_classes = Y_test.shape[1] # 26

model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu', data_format="channels_last"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print("DONE")
model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=10, batch_size=256, verbose=2)


# Final evaluation of the model
scores = model.evaluate(X_test,Y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))

model.save('weights.model')

Answer 1

所以问题是您的数据结构不正确。查看下面的解决方案：

使用熊猫读取数据：

data = pd.read_csv('/users/vpolimenov/Downloads/A_Z Handwritten Data.csv')
data.shape
# shape: (372450, 785)

获取X和y：

data.rename(columns={'0':'label'}, inplace=True)

X = data.drop('label',axis = 1)
y = data['label']

拆分并缩放：

X_train, X_test, y_train, y_test = train_test_split(X,y)

standard_scaler = MinMaxScaler()
standard_scaler.fit(X_train)

X_train = standard_scaler.transform(X_train)
X_test = standard_scaler.transform(X_test)

这就是魔力：

X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)

X_train.shape
# (279337, 28, 28, 1)

这是您的模特：

num_classes = y_test.shape[1] # 26

model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu', data_format="channels_last"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print("DONE")
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=256, verbose=2) # WHERE I GET THE ERROR

模型摘要：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_25 (Conv2D)           (None, 24, 24, 32)        832       
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 12, 12, 32)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 12, 12, 32)        0         
_________________________________________________________________
flatten_25 (Flatten)         (None, 4608)              0         
_________________________________________________________________
dense_42 (Dense)             (None, 128)               589952    
_________________________________________________________________
dense_43 (Dense)             (None, 26)                3354      
=================================================================
Total params: 594,138
Trainable params: 594,138
Non-trainable params: 0

在第二个时间段后，我已将其停止，但您可以看到它正在工作：

Train on 279337 samples, validate on 93113 samples
Epoch 1/10
 - 80s - loss: 0.2478 - acc: 0.9308 - val_loss: 0.1021 - val_acc: 0.9720
Epoch 2/10
 - 273s - loss: 0.0890 - acc: 0.9751 - val_loss: 0.0716 - val_acc: 0.9803
Epoch 3/10

注意：

由于网络中大量的参数，安装所需的时间太长。您可以尝试减少这些损失，并获得更快，更高效的网络。

拟合模型时出错-预期conv2d_3_input具有4维

1 个答案: