拟合模型时出错 - 预期 conv2d_3_input 有 4 个维度
Error fitting the model - expected conv2d_3_input to have 4 dimensions
我写信是为了使用此处给出的数据集构建一个模型来预测手写字符 (https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format)
编辑：（进行评论中建议的更改后）
我现在得到的错误：ValueError: Error when checking input: expected conv2d_4_input to have shape (28, 28, 1) but got array with shape (249542, 784, 1)
在下面找到 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')
所以问题是您的数据结构不正确。看看下面的解决方案：
用pandas读取数据：
data = pd.read_csv('/users/vpolimenov/Downloads/A_Z Handwritten Data.csv')
data.shape
# shape: (372450, 785)
得到你的 X 和 y：
data.rename(columns={'0':'label'}, inplace=True)
X = data.drop('label',axis = 1)
y = data['label']
拆分和缩放：
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)
神奇之处在于：
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)
这是您的模型：
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
您的模型摘要：
_________________________________________________________________
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
我在第二个 epoch 后停止了它，但你可以看到它在工作：
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：
由于您的网络中的参数数量巨大，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多 faster/efficient 网络。
我写信是为了使用此处给出的数据集构建一个模型来预测手写字符 (https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format)
编辑：（进行评论中建议的更改后）
我现在得到的错误：ValueError: Error when checking input: expected conv2d_4_input to have shape (28, 28, 1) but got array with shape (249542, 784, 1)
在下面找到 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')
所以问题是您的数据结构不正确。看看下面的解决方案：
用pandas读取数据：
data = pd.read_csv('/users/vpolimenov/Downloads/A_Z Handwritten Data.csv')
data.shape
# shape: (372450, 785)
得到你的 X 和 y：
data.rename(columns={'0':'label'}, inplace=True)
X = data.drop('label',axis = 1)
y = data['label']
拆分和缩放：
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)
神奇之处在于：
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)
这是您的模型：
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
您的模型摘要：
_________________________________________________________________
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
我在第二个 epoch 后停止了它，但你可以看到它在工作：
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：
由于您的网络中的参数数量巨大，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多 faster/efficient 网络。