1 Import data¶
In [2]:
dataset = pd.read_csv("datasets/Churn_Modelling.csv")
In [3]:
dataset.head()
Out[3]:
RowNumber | CustomerId | Surname | CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | Exited | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 15634602 | Hargrave | 619 | France | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 | 1 |
1 | 2 | 15647311 | Hill | 608 | Spain | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 | 0 |
2 | 3 | 15619304 | Onio | 502 | France | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 | 1 |
3 | 4 | 15701354 | Boni | 699 | France | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 | 0 |
4 | 5 | 15737888 | Mitchell | 850 | Spain | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 | 0 |
In [4]:
X = dataset.iloc[:,3:13]
y = dataset.iloc[:,13]
In [5]:
X.head()
Out[5]:
CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 619 | France | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
1 | 608 | Spain | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
2 | 502 | France | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
3 | 699 | France | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
4 | 850 | Spain | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
In [6]:
y.head()
Out[6]:
0 1 1 0 2 1 3 0 4 0 Name: Exited, dtype: int64
2 Encode categorical independent variables¶
In [7]:
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
label_encoder_geo = LabelEncoder()
X.iloc[:,1] = label_encoder_geo.fit_transform(X.iloc[:,1])
In [8]:
X.head()
Out[8]:
CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 619 | 0 | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
1 | 608 | 2 | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
2 | 502 | 0 | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
3 | 699 | 0 | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
4 | 850 | 2 | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
In [9]:
label_encoder_sex = LabelEncoder()
X.iloc[:,2] = label_encoder_sex.fit_transform(X.iloc[:,2])
In [10]:
X.head()
Out[10]:
CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 619 | 0 | 0 | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
1 | 608 | 2 | 0 | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
2 | 502 | 0 | 0 | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
3 | 699 | 0 | 0 | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
4 | 850 | 2 | 0 | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
In [11]:
onehotencoder = OneHotEncoder(categorical_features=[1])
X = onehotencoder.fit_transform(X).toarray()
In [12]:
X
Out[12]:
array([[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.01348880e+05], [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00, ..., 0.00000000e+00, 1.00000000e+00, 1.12542580e+05], [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 1.00000000e+00, 0.00000000e+00, 1.13931570e+05], ..., [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 1.00000000e+00, 4.20855800e+04], [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00, ..., 1.00000000e+00, 0.00000000e+00, 9.28885200e+04], [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 1.00000000e+00, 0.00000000e+00, 3.81907800e+04]])
In [13]:
X[0]
Out[13]:
array([ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.19000000e+02, 0.00000000e+00, 4.20000000e+01, 2.00000000e+00, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.01348880e+05])
Let's delete one categorical variable to avoid dummy variable trap¶
In [14]:
X = X[:,1:]
In [15]:
from sklearn.model_selection import train_test_split
In [16]:
X_train,X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 10)
Feature scaling is compulsory for ANN¶
In [17]:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
In [18]:
X_test = sc.fit_transform(X_test)
Let's make ANN¶
In [19]:
import keras
Using TensorFlow backend.
In [20]:
from keras.models import Sequential
from keras.layers import Dense
Let's do kfold cross validation¶
In [21]:
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
In [22]:
def build_classifier():
classifier = Sequential()
classifier.add(Dense(output_dim = 6, init = 'uniform',activation = 'relu', input_dim = 11))
classifier.add(Dense(output_dim = 6, init = 'uniform',activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform',activation = 'sigmoid'))
classifier.compile(optimizer='adam',loss ='binary_crossentropy', metrics = ['accuracy'])
return classifier
In [23]:
classifier = KerasClassifier(build_fn=build_classifier, batch_size = 10, epochs = 20)
In [24]:
accuracies = cross_val_score(estimator=classifier,X= X_train, y= y_train, cv = 10, n_jobs=-1)
Epoch 1/20 Epoch 1/20 Epoch 1/20 Epoch 1/20 Epoch 1/20 Epoch 1/20 Epoch 1/20 Epoch 1/20 5930/7200 [=======================>......] - ETA: 0s - loss: 0.5049 - acc: 0.7993 Epoch 2/20 7200/7200 [==============================] - 3s - loss: 0.4897 - acc: 0.7976 Epoch 2/20 6720/7200 [===========================>..] - ETA: 0s - loss: 0.4966 - acc: 0.7976Epoch 2/20 6970/7200 [============================>.] - ETA: 0s - loss: 0.4914 - acc: 0.7989 70/7200 [..............................] - ETA: 6s - loss: 0.5474 - acc: 0.7429Epoch 2/20 180/7200 [..............................] - ETA: 4s - loss: 0.4203 - acc: 0.8056 390/7200 [>.............................] - ETA: 3s - loss: 0.4445 - acc: 0.7872 Epoch 2/20 6570/7200 [==========================>...] - ETA: 0s - loss: 0.4978 - acc: 0.7992Epoch 2/20 330/7200 [>.............................] - ETA: 3s - loss: 0.4336 - acc: 0.7939 660/7200 [=>............................] - ETA: 3s - loss: 0.4388 - acc: 0.7894Epoch 2/20 1100/7200 [===>..........................] - ETA: 3s - loss: 0.4425 - acc: 0.7845 1060/7200 [===>..........................] - ETA: 3s - loss: 0.4344 - acc: 0.7943Epoch 2/20 7200/7200 [==============================] - 3s - loss: 0.4250 - acc: 0.7975 Epoch 3/20 7200/7200 [==============================] - 3s - loss: 0.4267 - acc: 0.7993 6640/7200 [==========================>...] - ETA: 0s - loss: 0.4240 - acc: 0.7974Epoch 3/20 6240/7200 [=========================>....] - ETA: 0s - loss: 0.4265 - acc: 0.7974 80/7200 [..............................] - ETA: 5s - loss: 0.4717 - acc: 0.8125Epoch 3/20 10/7200 [..............................] - ETA: 2s - loss: 0.3999 - acc: 0.9000 6970/7200 [============================>.] - ETA: 0s - loss: 0.4244 - acc: 0.7987Epoch 3/20 690/7200 [=>............................] - ETA: 3s - loss: 0.4352 - acc: 0.8000 420/7200 [>.............................] - ETA: 4s - loss: 0.4503 - acc: 0.7976Epoch 3/20 450/7200 [>.............................] - ETA: 3s - loss: 0.4309 - acc: 0.8022 790/7200 [==>...........................] - ETA: 3s - loss: 0.4397 - acc: 0.7911Epoch 3/20 1020/7200 [===>..........................] - ETA: 3s - loss: 0.4409 - acc: 0.7853 Epoch 3/20 1420/7200 [====>.........................] - ETA: 3s - loss: 0.4423 - acc: 0.7775 1090/7200 [===>..........................] - ETA: 3s - loss: 0.4501 - acc: 0.7771Epoch 3/20 6910/7200 [===========================>..] - ETA: 0s - loss: 0.4161 - acc: 0.8013 7000/7200 [============================>.] - ETA: 0s - loss: 0.4191 - acc: 0.7979Epoch 4/20 5940/7200 [=======================>......] - ETA: 0s - loss: 0.4244 - acc: 0.7931 6620/7200 [==========================>...] - ETA: 0s - loss: 0.4219 - acc: 0.7982Epoch 4/20 6120/7200 [========================>.....] - ETA: 0s - loss: 0.4234 - acc: 0.7940 6260/7200 [=========================>....] - ETA: 0s - loss: 0.4213 - acc: 0.7989Epoch 4/20 6380/7200 [=========================>....] - ETA: 0s - loss: 0.4205 - acc: 0.7998 340/7200 [>.............................] - ETA: 3s - loss: 0.4381 - acc: 0.8147Epoch 4/20 6590/7200 [==========================>...] - ETA: 0s - loss: 0.4202 - acc: 0.7997 450/7200 [>.............................] - ETA: 3s - loss: 0.4318 - acc: 0.8067Epoch 4/20 390/7200 [>.............................] - ETA: 3s - loss: 0.4586 - acc: 0.7949 640/7200 [=>............................] - ETA: 3s - loss: 0.4429 - acc: 0.8047Epoch 4/20 1050/7200 [===>..........................] - ETA: 3s - loss: 0.4400 - acc: 0.7914 570/7200 [=>............................] - ETA: 3s - loss: 0.4676 - acc: 0.7982Epoch 4/20 110/7200 [..............................] - ETA: 3s - loss: 0.4381 - acc: 0.8000 1250/7200 [====>.........................] - ETA: 3s - loss: 0.4409 - acc: 0.7904Epoch 4/20 7200/7200 [==============================] - 3s - loss: 0.4154 - acc: 0.8093 Epoch 5/20 6850/7200 [===========================>..] - ETA: 0s - loss: 0.4176 - acc: 0.8055 6860/7200 [===========================>..] - ETA: 0s - loss: 0.4171 - acc: 0.8086Epoch 5/20 7060/7200 [============================>.] - ETA: 0s - loss: 0.4154 - acc: 0.8099 190/7200 [..............................] - ETA: 4s - loss: 0.3924 - acc: 0.8105Epoch 5/20 6200/7200 [========================>.....] - ETA: 0s - loss: 0.4163 - acc: 0.8087 Epoch 5/20 320/7200 [>.............................] - ETA: 4s - loss: 0.3776 - acc: 0.8281Epoch 5/20 7200/7200 [==============================] - 3s - loss: 0.4128 - acc: 0.8260 490/7200 [=>............................] - ETA: 4s - loss: 0.4055 - acc: 0.8143Epoch 5/20 750/7200 [==>...........................] - ETA: 3s - loss: 0.3717 - acc: 0.8293 6980/7200 [============================>.] - ETA: 0s - loss: 0.4149 - acc: 0.8103Epoch 5/20 150/7200 [..............................] - ETA: 4s - loss: 0.4206 - acc: 0.8067 930/7200 [==>...........................] - ETA: 3s - loss: 0.4085 - acc: 0.8312Epoch 5/20 7200/7200 [==============================] - 4s - loss: 0.4121 - acc: 0.8299 6770/7200 [===========================>..] - ETA: 0s - loss: 0.4081 - acc: 0.8374Epoch 6/20 Epoch 6/20 110/7200 [..............................] - ETA: 3s - loss: 0.3680 - acc: 0.8727 Epoch 6/20 6060/7200 [========================>.....] - ETA: 0s - loss: 0.4135 - acc: 0.8302 7060/7200 [============================>.] - ETA: 0s - loss: 0.4077 - acc: 0.8368Epoch 6/20 350/7200 [>.............................] - ETA: 3s - loss: 0.3946 - acc: 0.8429Epoch 6/20 570/7200 [=>............................] - ETA: 3s - loss: 0.4086 - acc: 0.8368 6660/7200 [==========================>...] - ETA: 0s - loss: 0.4146 - acc: 0.8254Epoch 6/20 820/7200 [==>...........................] - ETA: 3s - loss: 0.4227 - acc: 0.8171 Epoch 6/20 1020/7200 [===>..........................] - ETA: 3s - loss: 0.4064 - acc: 0.8333 Epoch 6/20 7200/7200 [==============================] - 4s - loss: 0.4107 - acc: 0.8292 7200/7200 [==============================] - 4s - loss: 0.4098 - acc: 0.8318 Epoch 7/20 6220/7200 [========================>.....] - ETA: 0s - loss: 0.4175 - acc: 0.8251 6990/7200 [============================>.] - ETA: 0s - loss: 0.4108 - acc: 0.8288Epoch 7/20 100/7200 [..............................] - ETA: 3s - loss: 0.4096 - acc: 0.8200 6910/7200 [===========================>..] - ETA: 0s - loss: 0.4066 - acc: 0.8352Epoch 7/20 7200/7200 [==============================] - 4s - loss: 0.4118 - acc: 0.8289 6050/7200 [========================>.....] - ETA: 0s - loss: 0.4085 - acc: 0.8311 7040/7200 [============================>.] - ETA: 0s - loss: 0.4047 - acc: 0.8365Epoch 7/20 310/7200 [>.............................] - ETA: 3s - loss: 0.3850 - acc: 0.8355Epoch 7/20 150/7200 [..............................] - ETA: 5s - loss: 0.3484 - acc: 0.8533 Epoch 7/20 820/7200 [==>...........................] - ETA: 3s - loss: 0.4010 - acc: 0.8207 870/7200 [==>...........................] - ETA: 3s - loss: 0.4014 - acc: 0.8230Epoch 7/20 7200/7200 [==============================] - 4s - loss: 0.4098 - acc: 0.8312 1580/7200 [=====>........................] - ETA: 2s - loss: 0.4065 - acc: 0.8228Epoch 7/20 6810/7200 [===========================>..] - ETA: 0s - loss: 0.4105 - acc: 0.8325 5610/7200 [======================>.......] - ETA: 0s - loss: 0.4047 - acc: 0.8332Epoch 8/20 10/7200 [..............................] - ETA: 3s - loss: 0.2223 - acc: 0.9000 7060/7200 [============================>.] - ETA: 0s - loss: 0.4100 - acc: 0.8319Epoch 8/20 5760/7200 [=======================>......] - ETA: 0s - loss: 0.4051 - acc: 0.8325Epoch 8/20 7200/7200 [==============================] - 4s - loss: 0.4097 - acc: 0.8321 6460/7200 [=========================>....] - ETA: 0s - loss: 0.4131 - acc: 0.8319Epoch 8/20 7200/7200 [==============================] - 4s - loss: 0.4024 - acc: 0.8386 450/7200 [>.............................] - ETA: 3s - loss: 0.3752 - acc: 0.8556Epoch 8/20 430/7200 [>.............................] - ETA: 3s - loss: 0.3716 - acc: 0.8442 6130/7200 [========================>.....] - ETA: 0s - loss: 0.4065 - acc: 0.8323Epoch 8/20 590/7200 [=>............................] - ETA: 3s - loss: 0.3754 - acc: 0.8610 1120/7200 [===>..........................] - ETA: 3s - loss: 0.3938 - acc: 0.8393Epoch 8/20 900/7200 [==>...........................] - ETA: 3s - loss: 0.3869 - acc: 0.8567 Epoch 8/20 6990/7200 [============================>.] - ETA: 0s - loss: 0.4041 - acc: 0.8369 Epoch 9/20 7200/7200 [==============================] - 4s - loss: 0.4055 - acc: 0.8357 5760/7200 [=======================>......] - ETA: 0s - loss: 0.4052 - acc: 0.8352Epoch 9/20 7160/7200 [============================>.] - ETA: 0s - loss: 0.4084 - acc: 0.8325 Epoch 9/20 6210/7200 [========================>.....] - ETA: 0s - loss: 0.4069 - acc: 0.8346 330/7200 [>.............................] - ETA: 5s - loss: 0.5118 - acc: 0.7909Epoch 9/20 440/7200 [>.............................] - ETA: 4s - loss: 0.4802 - acc: 0.8023 240/7200 [>.............................] - ETA: 3s - loss: 0.4665 - acc: 0.8208Epoch 9/20 920/7200 [==>...........................] - ETA: 3s - loss: 0.4135 - acc: 0.8293 Epoch 9/20 1370/7200 [====>.........................] - ETA: 3s - loss: 0.4108 - acc: 0.8328 1550/7200 [=====>........................] - ETA: 3s - loss: 0.4196 - acc: 0.8297Epoch 9/20 1200/7200 [====>.........................] - ETA: 3s - loss: 0.4226 - acc: 0.8233 1440/7200 [=====>........................] - ETA: 3s - loss: 0.4106 - acc: 0.8375Epoch 9/20 7200/7200 [==============================] - 3s - loss: 0.4052 - acc: 0.8354 Epoch 10/20 430/7200 [>.............................] - ETA: 4s - loss: 0.4199 - acc: 0.8326 5960/7200 [=======================>......] - ETA: 0s - loss: 0.4037 - acc: 0.8357Epoch 10/20 5900/7200 [=======================>......] - ETA: 0s - loss: 0.4043 - acc: 0.8358Epoch 10/20 60/7200 [..............................] - ETA: 6s - loss: 0.4578 - acc: 0.7833 7200/7200 [==============================] - 3s - loss: 0.3996 - acc: 0.8393 Epoch 10/20 6770/7200 [===========================>..] - ETA: 0s - loss: 0.4082 - acc: 0.8325 580/7200 [=>............................] - ETA: 4s - loss: 0.4044 - acc: 0.8345Epoch 10/20 680/7200 [=>............................] - ETA: 4s - loss: 0.4092 - acc: 0.8279 580/7200 [=>............................] - ETA: 3s - loss: 0.4135 - acc: 0.8379Epoch 10/20 1090/7200 [===>..........................] - ETA: 3s - loss: 0.3873 - acc: 0.8459 1110/7200 [===>..........................] - ETA: 3s - loss: 0.3889 - acc: 0.8505Epoch 10/20 7200/7200 [==============================] - 4s - loss: 0.4076 - acc: 0.8346 Epoch 10/20 6740/7200 [===========================>..] - ETA: 0s - loss: 0.4036 - acc: 0.8362 Epoch 11/20 6990/7200 [============================>.] - ETA: 0s - loss: 0.4037 - acc: 0.8365 7120/7200 [============================>.] - ETA: 0s - loss: 0.4022 - acc: 0.8369Epoch 11/20 7200/7200 [==============================] - 4s - loss: 0.3984 - acc: 0.8382 6010/7200 [========================>.....] - ETA: 0s - loss: 0.4051 - acc: 0.8361Epoch 11/20 80/7200 [..............................] - ETA: 6s - loss: 0.4434 - acc: 0.8000 Epoch 11/20 6610/7200 [==========================>...] - ETA: 0s - loss: 0.4023 - acc: 0.8384 6210/7200 [========================>.....] - ETA: 0s - loss: 0.4012 - acc: 0.8374Epoch 11/20 7200/7200 [==============================] - 4s - loss: 0.4046 - acc: 0.8364 950/7200 [==>...........................] - ETA: 3s - loss: 0.3857 - acc: 0.8411Epoch 11/20 7130/7200 [============================>.] - ETA: 0s - loss: 0.4056 - acc: 0.8355 1190/7200 [===>..........................] - ETA: 3s - loss: 0.3802 - acc: 0.8395Epoch 11/20 7200/7200 [==============================] - 4s - loss: 0.4064 - acc: 0.8350 10/7200 [..............................] - ETA: 6s - loss: 0.2716 - acc: 0.9000Epoch 11/20 6040/7200 [========================>.....] - ETA: 0s - loss: 0.4007 - acc: 0.8382Epoch 13/20 Epoch 13/20 350/7200 [>.............................] - ETA: 4s - loss: 0.4089 - acc: 0.8286 6000/7200 [========================>.....] - ETA: 0s - loss: 0.4015 - acc: 0.8372Epoch 13/20 360/7200 [>.............................] - ETA: 4s - loss: 0.4113 - acc: 0.8389 740/7200 [==>...........................] - ETA: 4s - loss: 0.4180 - acc: 0.8351Epoch 13/20 6910/7200 [===========================>..] - ETA: 0s - loss: 0.4032 - acc: 0.8359 Epoch 13/20 1600/7200 [=====>........................] - ETA: 3s - loss: 0.3940 - acc: 0.8419 1290/7200 [====>.........................] - ETA: 3s - loss: 0.4137 - acc: 0.8326Epoch 13/20 5350/7200 [=====================>........] - ETA: 1s - loss: 0.3960 - acc: 0.8394 6770/7200 [===========================>..] - ETA: 0s - loss: 0.4024 - acc: 0.8366Epoch 15/20 7200/7200 [==============================] - 4s - loss: 0.3950 - acc: 0.8382 Epoch 15/20 7200/7200 [==============================] - 4s - loss: 0.4015 - acc: 0.8372 6110/7200 [========================>.....] - ETA: 0s - loss: 0.4004 - acc: 0.8401Epoch 15/20 6710/7200 [==========================>...] - ETA: 0s - loss: 0.3978 - acc: 0.8399 530/7200 [=>............................] - ETA: 4s - loss: 0.3898 - acc: 0.8302Epoch 15/20 6350/7200 [=========================>....] - ETA: 0s - loss: 0.3997 - acc: 0.8402 5960/7200 [=======================>......] - ETA: 0s - loss: 0.3989 - acc: 0.8384Epoch 15/20 560/7200 [=>............................] - ETA: 4s - loss: 0.3755 - acc: 0.8411 630/7200 [=>............................] - ETA: 4s - loss: 0.3916 - acc: 0.8365Epoch 15/20 1040/7200 [===>..........................] - ETA: 4s - loss: 0.4027 - acc: 0.8279 Epoch 15/20 430/7200 [>.............................] - ETA: 5s - loss: 0.4077 - acc: 0.8395 Epoch 15/20 6450/7200 [=========================>....] - ETA: 0s - loss: 0.4038 - acc: 0.8366 5230/7200 [====================>.........] - ETA: 1s - loss: 0.4001 - acc: 0.8377Epoch 16/20 7160/7200 [============================>.] - ETA: 0s - loss: 0.4027 - acc: 0.8363 6430/7200 [=========================>....] - ETA: 0s - loss: 0.4046 - acc: 0.8364Epoch 16/20 6120/7200 [========================>.....] - ETA: 0s - loss: 0.4037 - acc: 0.8364 Epoch 16/20 Epoch 16/20 6190/7200 [========================>.....] - ETA: 0s - loss: 0.4045 - acc: 0.8346 6900/7200 [===========================>..] - ETA: 0s - loss: 0.4014 - acc: 0.8378Epoch 16/20 7200/7200 [==============================] - 4s - loss: 0.4005 - acc: 0.8382 750/7200 [==>...........................] - ETA: 4s - loss: 0.4081 - acc: 0.8360Epoch 16/20 1720/7200 [======>.......................] - ETA: 3s - loss: 0.4145 - acc: 0.8291 400/7200 [>.............................] - ETA: 4s - loss: 0.4244 - acc: 0.8275Epoch 16/20 680/7200 [=>............................] - ETA: 4s - loss: 0.4109 - acc: 0.8338 Epoch 16/20 6500/7200 [==========================>...] - ETA: 0s - loss: 0.4013 - acc: 0.8392 570/7200 [=>............................] - ETA: 3s - loss: 0.4218 - acc: 0.8263Epoch 17/20 7200/7200 [==============================] - 4s - loss: 0.4015 - acc: 0.8382 6040/7200 [========================>.....] - ETA: 0s - loss: 0.4025 - acc: 0.8379 7130/7200 [============================>.] - ETA: 0s - loss: 0.3994 - acc: 0.8374Epoch 17/20 6080/7200 [========================>.....] - ETA: 0s - loss: 0.4085 - acc: 0.8347Epoch 17/20 120/7200 [..............................] - ETA: 3s - loss: 0.3848 - acc: 0.8583 6650/7200 [==========================>...] - ETA: 0s - loss: 0.4001 - acc: 0.8395Epoch 17/20 6780/7200 [===========================>..] - ETA: 0s - loss: 0.4034 - acc: 0.8364 Epoch 17/20 440/7200 [>.............................] - ETA: 4s - loss: 0.4192 - acc: 0.8227 970/7200 [===>..........................] - ETA: 4s - loss: 0.4100 - acc: 0.8340Epoch 17/20 7200/7200 [==============================] - 4s - loss: 0.4004 - acc: 0.8369 Epoch 17/20 7200/7200 [==============================] - 4s - loss: 0.3970 - acc: 0.8397 5770/7200 [=======================>......] - ETA: 0s - loss: 0.4039 - acc: 0.8348 7200/7200 [==============================] - 4s - loss: 0.3994 - acc: 0.8383 7200/7200 [==============================] - 4s - loss: 0.3972 - acc: 0.8371 7200/7200 [==============================] - 4s - loss: 0.3975 - acc: 0.8378 7200/7200 [==============================] - 4s - loss: 0.3978 - acc: 0.8387 7200/7200 [==============================] - 4s - loss: 0.3995 - acc: 0.8368 730/800 [==========================>...] - ETA: 0sEpoch 1/20 Epoch 1/20 7200/7200 [==============================] - 2s - loss: 0.4881 - acc: 0.7960 Epoch 2/20 7200/7200 [==============================] - 2s - loss: 0.4905 - acc: 0.7957 Epoch 2/20 7200/7200 [==============================] - 2s - loss: 0.4244 - acc: 0.7968 Epoch 3/20 7200/7200 [==============================] - 2s - loss: 0.4265 - acc: 0.7965 Epoch 3/20 7200/7200 [==============================] - 2s - loss: 0.4188 - acc: 0.7983 Epoch 4/20 7200/7200 [==============================] - 2s - loss: 0.4215 - acc: 0.7965 Epoch 4/20 7200/7200 [==============================] - 2s - loss: 0.4143 - acc: 0.8257 Epoch 5/20 7200/7200 [==============================] - 2s - loss: 0.4173 - acc: 0.8107 Epoch 5/20 7200/7200 [==============================] - 2s - loss: 0.4114 - acc: 0.8317 Epoch 6/20 7200/7200 [==============================] - 2s - loss: 0.4148 - acc: 0.8246 Epoch 6/20 7200/7200 [==============================] - 1s - loss: 0.4101 - acc: 0.8329 Epoch 7/20 7200/7200 [==============================] - 1s - loss: 0.4135 - acc: 0.8296 Epoch 7/20 7200/7200 [==============================] - 1s - loss: 0.4082 - acc: 0.8346 Epoch 8/20 7200/7200 [==============================] - 1s - loss: 0.4117 - acc: 0.8311 Epoch 8/20 7200/7200 [==============================] - 1s - loss: 0.4071 - acc: 0.8367 Epoch 9/20 7200/7200 [==============================] - 1s - loss: 0.4104 - acc: 0.8315 Epoch 9/20 7200/7200 [==============================] - 1s - loss: 0.4060 - acc: 0.8364 Epoch 10/20 7200/7200 [==============================] - 1s - loss: 0.4095 - acc: 0.8319 Epoch 10/20 7200/7200 [==============================] - 1s - loss: 0.4048 - acc: 0.8369 Epoch 11/20 7200/7200 [==============================] - 1s - loss: 0.4081 - acc: 0.8324 Epoch 11/20 7200/7200 [==============================] - 1s - loss: 0.4038 - acc: 0.8365 Epoch 12/20 7200/7200 [==============================] - 1s - loss: 0.4072 - acc: 0.8329 Epoch 12/20 7200/7200 [==============================] - 1s - loss: 0.4031 - acc: 0.8374 Epoch 13/20 7200/7200 [==============================] - 1s - loss: 0.4061 - acc: 0.8336 Epoch 13/20 7200/7200 [==============================] - 1s - loss: 0.4025 - acc: 0.8378 Epoch 14/20 7200/7200 [==============================] - 1s - loss: 0.4058 - acc: 0.8329 Epoch 14/20 7200/7200 [==============================] - 1s - loss: 0.4015 - acc: 0.8381 Epoch 15/20 7200/7200 [==============================] - 1s - loss: 0.4048 - acc: 0.8336 Epoch 15/20 7200/7200 [==============================] - 2s - loss: 0.4007 - acc: 0.8396 Epoch 16/20 7200/7200 [==============================] - 2s - loss: 0.4041 - acc: 0.8351 Epoch 16/20 7200/7200 [==============================] - 1s - loss: 0.4005 - acc: 0.8382 Epoch 17/20 7200/7200 [==============================] - 1s - loss: 0.4038 - acc: 0.8344 Epoch 17/20 7200/7200 [==============================] - 1s - loss: 0.4002 - acc: 0.8376 Epoch 18/20 7200/7200 [==============================] - 1s - loss: 0.4034 - acc: 0.8353 Epoch 18/20 7200/7200 [==============================] - 1s - loss: 0.3994 - acc: 0.8381 5980/7200 [=======================>......] - ETA: 0s - loss: 0.4016 - acc: 0.8363Epoch 19/20 7200/7200 [==============================] - 1s - loss: 0.4025 - acc: 0.8346 Epoch 19/20 7200/7200 [==============================] - 1s - loss: 0.3992 - acc: 0.8390 Epoch 20/20 7200/7200 [==============================] - 1s - loss: 0.4024 - acc: 0.8349 Epoch 20/20 7200/7200 [==============================] - 1s - loss: 0.3979 - acc: 0.8381 7200/7200 [==============================] - 1s - loss: 0.4015 - acc: 0.8349 10/800 [..............................] - ETA: 2s
In [25]:
accuracies
Out[25]:
array([ 0.82999999, 0.82875 , 0.845 , 0.83624999, 0.83499999, 0.83874999, 0.83875 , 0.83249999, 0.82499999, 0.85624999])
In [26]:
accuracies.mean()
Out[26]:
0.83662499487400055
In [27]:
accuracies.std()
Out[27]:
0.0085156105222930732
No comments :
Post a Comment