问题
I am currently working on a project involving training a regression model, saving it and then loading it to make further predictions using that model. However I'm having a problem. Each time that I model.predict on images it gives out the same predictions. I am not entirely sure what the problem is, maybe it's in the training stage or i'm just doing something wrong. I was following this tutorial
All of the files are in this github repo
Here are some bits from the code: (This part is training the model and saving it)
model = create_cnn(400, 400, 3, regress=True)
opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss="mean_absolute_percentage_error", optimizer=opt)
model.fit(X, Y, epochs=70, batch_size=8)
model.save("D:/statispic2/final-statispic_model.hdf5")
The next code part is from loading the model and making predictions.
model = load_model("D:/statispic2/statispic_model.hdf5") # Loading the model
prediction = model.predict(images_ready_for_prediction) #images ready for prediction include a numpy array
#that is loaded with the images just like I loaded them for the training stage.
print(prediction_list)
After trying it out this is the output prediction from the model:
[[0.05169942] # I gave it 5 images as parameters
[0.05169942]
[0.05169942]
[0.05169942]
[0.05169942]]
If anything is unclear, or you would like to see some more code, please let me know.
回答1:
Cnn is deep learning. You use regression for calculating a number, like the price of a car.
回答2:
No, no no! Regression is completely different from CNN. Do a little research and the differences will quickly become apparent. In the meantime, I'll share two code samples with you right here.
Regression:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#%matplotlib inline
import sklearn
from sklearn.datasets import load_boston
boston = load_boston()
# Now we will load the data into a pandas dataframe and then will print the first few rows of the data using the head() function.
bos = pd.DataFrame(boston.data)
bos.head()
bos.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']
bos.head()
bos['MEDV'] = boston.target
bos.describe()
bos.isnull().sum()
sns.distplot(bos['MEDV'])
plt.show()
sns.pairplot(bos)
corr_mat = bos.corr().round(2)
sns.heatmap(data=corr_mat, annot=True)
sns.lmplot(x = 'RM', y = 'MEDV', data = bos)
X = bos[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX','PTRATIO', 'B', 'LSTAT']]
y = bos['MEDV']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 10)
# Training the Model
# We will now train our model using the LinearRegression function from the sklearn library.
from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train, y_train)
# Prediction
# We will now make prediction on the test data using the LinearRegression function and plot a scatterplot between the test data and the predicted value.
prediction = lm.predict(X_test)
plt.scatter(y_test, prediction)
df1 = pd.DataFrame({'Actual': y_test, 'Predicted':prediction})
df2 = df1.head(10)
df2
df2.plot(kind = 'bar')
from sklearn import metrics
from sklearn.metrics import r2_score
print('MAE', metrics.mean_absolute_error(y_test, prediction))
print('MSE', metrics.mean_squared_error(y_test, prediction))
print('RMSE', np.sqrt(metrics.mean_squared_error(y_test, prediction)))
print('R squared error', r2_score(y_test, prediction))
Result:
MAE 4.061419182954711
MSE 34.413968453138565
RMSE 5.866341999333023
R squared error 0.6709339839115628
CNN:
# keras imports for the dataset and building our neural network
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten
from keras.utils import np_utils
# to calculate accuracy
from sklearn.metrics import accuracy_score
# loading the dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# building the input vector from the 28x28 pixels
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# normalizing the data to help with the training
X_train /= 255
X_test /= 255
# one-hot encoding using keras' numpy-related utilities
n_classes = 10
print("Shape before one-hot encoding: ", y_train.shape)
Y_train = np_utils.to_categorical(y_train, n_classes)
Y_test = np_utils.to_categorical(y_test, n_classes)
print("Shape after one-hot encoding: ", Y_train.shape)
# building a linear stack of layers with the sequential model
model = Sequential()
# convolutional layer
model.add(Conv2D(25, kernel_size=(3,3), strides=(1,1), padding='valid', activation='relu', input_shape=(28,28,1)))
model.add(MaxPool2D(pool_size=(1,1)))
# flatten output of conv
model.add(Flatten())
# hidden layer
model.add(Dense(100, activation='relu'))
# output layer
model.add(Dense(10, activation='softmax'))
# compiling the sequential model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
# training the model for 10 epochs
model.fit(X_train, Y_train, batch_size=128, epochs=10, validation_data=(X_test, Y_test))
Result:
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 27s 451us/step - loss: 0.2037 - accuracy: 0.9400 - val_loss: 0.0866 - val_accuracy: 0.9745
Epoch 2/10
60000/60000 [==============================] - 27s 451us/step - loss: 0.0606 - accuracy: 0.9819 - val_loss: 0.0553 - val_accuracy: 0.9812
Epoch 3/10
60000/60000 [==============================] - 27s 445us/step - loss: 0.0352 - accuracy: 0.9892 - val_loss: 0.0533 - val_accuracy: 0.9824
Epoch 4/10
60000/60000 [==============================] - 27s 446us/step - loss: 0.0226 - accuracy: 0.9930 - val_loss: 0.0572 - val_accuracy: 0.9825
Epoch 5/10
60000/60000 [==============================] - 27s 448us/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.0516 - val_accuracy: 0.9834
Epoch 6/10
60000/60000 [==============================] - 27s 443us/step - loss: 0.0088 - accuracy: 0.9976 - val_loss: 0.0574 - val_accuracy: 0.9824
Epoch 7/10
60000/60000 [==============================] - 26s 442us/step - loss: 0.0089 - accuracy: 0.9973 - val_loss: 0.0526 - val_accuracy: 0.9847
Epoch 8/10
60000/60000 [==============================] - 26s 440us/step - loss: 0.0047 - accuracy: 0.9988 - val_loss: 0.0593 - val_accuracy: 0.9838
Epoch 9/10
60000/60000 [==============================] - 28s 469us/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.0559 - val_accuracy: 0.9836
Epoch 10/10
60000/60000 [==============================] - 27s 449us/step - loss: 0.0059 - accuracy: 0.9981 - val_loss: 0.0663 - val_accuracy: 0.9820
来源：https://stackoverflow.com/questions/61698373/machine-learning-regression-model-predicts-same-value-for-every-image