def test_continue_learning():
mlp = MLP(epochs=25,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
assert np.sum(y == mlp.predict(X)) == 144, np.sum(y == mlp.predict(X))
mlp.fit(X, y, init_params=False)
assert np.sum(y == mlp.predict(X)) == 150, np.sum(y == mlp.predict(X))
def _clf_mlp(trX,teX,trY,teY):
print "MLP"
print trX.shape,"trX shape"
print "Enter Layer for MLP"
layer=input()
# print "enter delIdx"
# delIdx=input()
# while(delIdx):
# trX=np.delete(trX,-1,axis=0)
# trY=np.delete(trY,-1,axis=0)
# delIdx=delIdx-1
print "factors",factors(trX.shape[0])
teY=teY.astype(np.int32)
trY=trY.astype(np.int32)
print trX.shape,"trX shape"
print "enter no of mini batch"
mini_batch=int(input())
mlp = TfMultiLayerPerceptron(eta=0.01,
epochs=100,
hidden_layers=layer,
activations=['relu' for i in range(len(layer))],
print_progress=3,
minibatches=mini_batch,
optimizer='adam',
random_seed=1)
mlp.fit(trX,trY)
pred=mlp.predict(teX)
print _f_count(teY),"test f count"
pred=pred.astype(np.int32)
print _f_count(pred),"pred f count"
conf_mat=confusion_matrix(teY, pred)
process_cm(conf_mat, to_print=True)
print precision_score(teY,pred),"Precision Score"
print recall_score(teY,pred),"Recall Score"
print roc_auc_score(teY,pred), "ROC_AUC"
def test_fail_minibatches():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=13,
random_seed=1)
mlp.fit(X, y)
assert (y == mlp.predict(X)).all()
def test_fail_minibatches():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=13,
random_seed=1)
mlp.fit(X, y)
assert (y == mlp.predict(X)).all()
def test_binary_sgd():
mlp = MLP(epochs=10,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=len(y_bin),
random_seed=1)
mlp.fit(X_bin, y_bin)
assert (y_bin == mlp.predict(X_bin)).all()
def test_binary_sgd():
mlp = MLP(epochs=10,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=len(y_bin),
random_seed=1)
mlp.fit(X_bin, y_bin)
assert (y_bin == mlp.predict(X_bin)).all()
def test_multiclass_gd_acc():
mlp = MLP(
epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer="gradientdescent",
activations=["logistic"],
minibatches=1,
random_seed=1,
)
mlp.fit(X, y)
assert (y == mlp.predict(X)).all()
def test_binary_gd_relu():
mlp = MLP(
epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer="gradientdescent",
activations=["relu"],
minibatches=1,
random_seed=1,
)
mlp.fit(X_bin, y_bin)
assert (y_bin == mlp.predict(X_bin)).all()
def _clf_mlp(trX, teX, trY, teY):
print "MLP"
print trX.shape, "trX shape"
print "Enter Layer for MLP"
layer = input()
# print "enter delIdx"
# delIdx=input()
# while(delIdx):
# trX=np.delete(trX,-1,axis=0)
# trY=np.delete(trY,-1,axis=0)
# delIdx=delIdx-1
print "factors", factors(trX.shape[0])
teY = teY.astype(np.int32)
trY = trY.astype(np.int32)
print trX.shape, "trX shape"
print "enter no of mini batch"
mini_batch = int(input())
mlp = TfMultiLayerPerceptron(
eta=0.01,
epochs=100,
hidden_layers=layer,
activations=['relu' for i in range(len(layer))],
print_progress=3,
minibatches=mini_batch,
optimizer='adam',
random_seed=1)
mlp.fit(trX, trY)
pred = mlp.predict(teX)
print _f_count(teY), "test f count"
pred = pred.astype(np.int32)
print _f_count(pred), "pred f count"
conf_mat = confusion_matrix(teY, pred)
process_cm(conf_mat, to_print=True)
print precision_score(teY, pred), "Precision Score"
print recall_score(teY, pred), "Recall Score"
print roc_auc_score(teY, pred), "ROC_AUC"
