Keras 拟合模型时出错-预期conv2d_3_输入有4个维度_Keras_Deep Learning_Conv Neural Network_Sequential

Keras 拟合模型时出错-预期conv2d_3_输入有4个维度

keras deep-learning

Keras 拟合模型时出错-预期conv2d_3_输入有4个维度,keras,deep-learning,conv-neural-network,sequential,Keras,Deep Learning,Conv Neural Network,Sequential,我写这篇文章的目的是建立一个模型，使用这里给出的数据集来预测手写字符（）编辑：（在作出评论中建议的更改后）我现在得到的错误：ValueError:检查输入时出错：预期conv2d\u 4\u输入具有形状（28,28,1），但得到具有形状的数组（249542,784,1）下面是CNN的代码： from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout f

我写这篇文章的目的是建立一个模型，使用这里给出的数据集来预测手写字符（）

编辑：（在作出评论中建议的更改后）

我现在得到的错误：

ValueError:检查输入时出错：预期conv2d\u 4\u输入具有形状（28,28,1），但得到具有形状的数组（249542,784,1）

下面是CNN的代码：

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')

因此，问题是您的数据结构不正确。请看下面的解决方案：

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)

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

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

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)

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)

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

_________________________________________________________________
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

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

获取您的X和y:

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

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

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

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)

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)

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

_________________________________________________________________
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

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

拆分和缩放：

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

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

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

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)

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)

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

_________________________________________________________________
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

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

这就是魔力：

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

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

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

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)

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)

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

_________________________________________________________________
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

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

这是您的型号：

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

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

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

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)

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)

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

_________________________________________________________________
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

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

您的模型摘要：

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

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

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

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)

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)

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

_________________________________________________________________
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

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

我在第二个纪元后停止了它，但您可以看到它正在工作：

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

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

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

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)

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)

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

_________________________________________________________________
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

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：

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

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

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

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)

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)

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

_________________________________________________________________
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

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

由于您的网络中有大量参数，因此需要很长时间才能适应。您可以尝试减少这些，并获得更快/更高效的网络。

因此，问题在于您的数据结构不正确。请看下面的解决方案：