recall = recall_m(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
# define the keras model
model = Sequential()
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
history = model.fit(np.asarray(X_train), np.asarray(y_train), epochs=15, batch_size=1)
# evaluate the keras model
_, accuracy = model.evaluate(np.asarray(X_test), np.asarray(y_test))
print('Accuracy: %.2f' % (accuracy*100))
# make class predictions with the model
expected = np.asarray(y_test)
predicted = model.predict_classes(np.asarray(X_test))
# summarize the fit of the model
classification_model = metrics.classification_report(expected, predicted)
confusion_model = metrics.confusion_matrix(expected, predicted)
accuracy_model = metrics.accuracy_score(expected, predicted)
print(classification_model)
print(confusion_model)
print("Accuracy NN with best parameters: ",accuracy_model)
#Save metrics
with open(save_report, "a") as text_file:
text_file.write("\nAccuracy NN with best parameters: ")
text_file.write(str(accuracy_model))
text_file.write("\nClassification NN with best parameters:\n")
text_file.write(classification_model)
# %% [markdown] # **The rectified linear unit (relu) activation function** is used as a good general activation function for the first two layers, while the sigmoid activation function is required for the final layer as the output you want (of whether a passenger survives or not) needs to be scaled in the range of 0-1 (the probability of a passenger surviving). # %% model.summary() # %% model.compile(optimizer="adam", loss='binary_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, batch_size=32, epochs=50) # %% [markdown] # ### With the model built and trained its now time to see how it performs against the test data. # %% y_pred = model.predict_classes(X_test) print(metrics.accuracy_score(y_test, y_pred)) # %% # %% # %%
training = model.fit(Xtrain.values,
ytrain.values,
validation_split=0.1,
epochs=200,
batch_size=10,
verbose=2)
model.summary()
"""## Testing the accuracy"""
accuracy = model.evaluate(Xtrain, ytrain)
print(accuracy)
# Resultate mit tatsächlichen Werten vergleichen
print(model.predict_classes(Xtest))
print("[", end="")
for i in ytest:
print(i, end=" ")
print("\b]")
"""## Plotting the results"""
from matplotlib import pyplot as plt
history = training
plt.figure(figsize=(15, 10))
plt.subplots_adjust(hspace=0.5)
plt.subplot(221)
