I have the following NN model using Keras:
import numpy as np
from keras import Sequential
from keras.layers import Dense
path = 'pima-indians-diabetes.data.csv'
dataset = np.loadtxt(path, delimiter=",")
X = dataset[:,0:8]
Y = dataset[:,8]
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
model = Sequential()
model.add(Dense(16, input_dim=8, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test))
Kindly, is it possible to extract the confusion matrix? How?
You can use scikit-learn:
y_pred = model.predict(X_test)
confusion_matrix = sklearn.metrics.confusion_matrix(y_test, np.rint(y_pred))
It can be done using TensorFlow (which is almost Keras =)).
You start by making predictions on your test set with your trained model:
predictions = model.predict(x_test)
Then you can import TensorFlow and use its confusion_matrix method as follows.
import tensorflow as tf
conf_matrix = tf.math.confusion_matrix(labels=y_test,
predictions=predictions)
More information in the TensorFlow documentation.
Related
I am trying to Save the entire model after each epoch using ModelCheckpoint callback.
After training if i am trying to load the saved model and evaluate, the model weights are not loaded. Why is this load_model not working for loading the model weights?
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
# Use smaller subset -- speeds things up
x_train = x_train[:10000]
y_train = y_train[:10000]
x_test = x_test[:1000]
y_test = y_test[:1000]
def get_test_accuracy(model, x_test, y_test):
test_loss, test_acc = model.evaluate(x=x_test, y=y_test, verbose=0)
print('accuracy: {acc:0.3f}'.format(acc=test_acc))
def get_new_model():
model = Sequential([
Conv2D(filters=16, input_shape=(32, 32, 3), kernel_size=(3, 3),
activation='relu', name='conv_1'),
Conv2D(filters=8, kernel_size=(3, 3), activation='relu', name='conv_2'),
MaxPooling2D(pool_size=(4, 4), name='pool_1'),
Flatten(name='flatten'),
Dense(units=32, activation='relu', name='dense_1'),
Dense(units=10, activation='softmax', name='dense_2')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
# Create Tensorflow checkpoint object
checkpoint_path = "model_checkpoints"
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=False,
save_freq="epoch",
verbose=1)
# Create and fit model with checkpoint
model = get_new_model()
model.fit(x_train,
y_train,
epochs=3,
callbacks=[checkpoint])
# Get the model's test accuracy
get_test_accuracy(model,x_test,y_test)
# Reload model from scratch
model = load_model(checkpoint_path)
get_test_accuracy(model,x_test,y_test)
The accuracy after loading the saved model load_model is not same as the accuracy for trained model.
i was trying to improve the accuracy and evaluate my Artificial Neural Network, but i encountered an issue that n_jobs = -1 of cross_val_score was not working,
i am using tensorflow on my cpu and my error was:-
BrokenProcessPool: A task has failed to un-serialize. Please ensure
that the arguments of the function are all picklable.
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
def build_classifier():
classifier = Sequential()
classifier.add(Dense(6, kernel_initializer='uniform', activation='relu', input_dim=11))
classifier.add(Dense(6, kernel_initializer='uniform', activation='relu'))
classifier.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, nb_epoch = 100)
accuracies = cross_val_score(estimator= classifier, X= x_train, y= y_train, cv = 10, n_jobs= 1)
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.datasets import mnist
import numpy
model = Sequential()
model.add(Dense(500,input_shape=(784,))) # 28*28=784
model.add(Activation('tanh')) # tanh
model.add(Dropout(0.5)) # 50% dropout
model.add(Dense(500)) # 500个
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, class_mode='categorical')
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1] * X_train.shape[2])
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1] * X_test.shape[2])
Y_train = (numpy.arange(10) == y_train[:, None]).astype(int)
Y_test = (numpy.arange(10) == y_test[:, None]).astype(int)
model.fit(X_train,Y_train,batch_size=200,epochs=50,shuffle=True,verbose=0,validation_split=0.3)
model.evaluate(X_test, Y_test, batch_size=200, verbose=0)
print("test set")
scores = model.evaluate(X_test,Y_test,batch_size=200,verbose=0)
print("")
print("The test loss is %f" % scores)
result = model.predict(X_test,batch_size=200,verbose=0)
I found this post Error when profiling keras models, which modifies the tensorflow library.
So, I checked Keras library code from the link. But could not find anything like ['class_mode'] to modify the keras library. Next, I tried running the code after re-installing keras, but even that didn't work.
I used anaconda to import Kreas， maybe I install wrong?
Can anyone suggest a solution for this?
remove class_mode='categorical',it runs
I am newbie on keras,
I try to follow the Keras tutorial for Multilayer Perceptron (MLP) for multi-class softmax classification, using my data set.
My data has 3 classes and only one feature, but I don't understand why the result always show just 0,3 of accuracy and the model predicted all training data as first class. then the confusion matrix is like this.
Confusion matrix
Here the coding:
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
import pandas as pd
import numpy as np
# Importing the dataset
dataset = pd.read_csv('StatusAll.csv')
X = dataset.iloc[:, 1:].values
y = dataset.iloc[:, 0:1].values
# Splitting the dataset into the Training set and Test set
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 = 0)
from keras.utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='tanh', input_dim=1))
model.add(Dropout(0.5))
model.add(Dense(64, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
history = model.fit(x_train, y_train,
epochs=100,
batch_size=128)
score = model.evaluate(x_test, y_test, batch_size=128)
print('Test score:', score[0])
print('Test accuracy:', score[1])
from sklearn import metrics
prediction = model.predict(x_test)
prediction = np.around(prediction)
y_test_non_category = [ np.argmax(t) for t in y_test ]
y_predict_non_category = [ np.argmax(t) for t in prediction ]
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test_non_category, y_predict_non_category)
print (conf_mat)
I hope I can get some advice, thanksss.
The x_train example
x_train
y_train before converted to categorical
enter image description here
Your final Dense layer has 4 outputs, it seems like you are classifying 4 instead of 3.
model.add(Dense(3, activation='softmax')) # Number of classes 3
It would be helpful to see sample data from x_train and y_train to make sure the pre-processing is correct. Because you have only 1 feature, a MLP might be overkill. A decision tree would be simpler unless you want to experiment with MLPs.
I recently got a Nvidia Card and wanted to try LSTM-Models with the new GPU-Support. Sadly I do not know much about LSTMs. And I build this little model to test it:
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Normalizer, StandardScaler
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config))
data = pd.read_excel("./data/google_data.xlsx", header=0)
X = data.drop("Close", axis=1)
y = data["Close"]
model = Sequential()
model.add(LSTM(units=10, activation='sigmoid', input_shape=(4,1),return_sequences=True))
model.add(Dropout(0.4))
model.add(Dense(10, activation="sigmoid"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer='adam', loss='mean_squared_error')
print(model.summary())
normalizer = StandardScaler()
normalizer.fit(X)
X = normalizer.fit_transform(X)
X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.20, shuffle=False)
model.fit(X, y, batch_size=64, epochs=40, verbose=1, validation_data=(X_test, y_test))
I always get an ValueError, I have tried the Inputshape from the Keras Docs w ith (batch_size,timesteps,features) but I still get the same ValueError.
I guess it is probably a quite dumb problem, but a newbie like me could need some help. Thanks!