tick_marks = np.arange(len(CM_LABELS))
plt.xticks(tick_marks, CM_LABELS, rotation=45)
plt.yticks(tick_marks, CM_LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
NUM_PER_CATEGORY = 5500
print('loading data....')
print(str(NUM_PER_CATEGORY) + ' examples per category')
start_time = time.time()
# images, labels = prep.get_crowdflower(NUM_PER_CATEGORY)
images, labels = prep.get_data(NUM_PER_CATEGORY)
images = images.astype('float32')
train_x, test_x, train_y, test_y = train_test_split(
images, labels, train_size=.7, random_state=20160319)
print('Total data load time:')
print('---------------------')
print(time.time() - start_time)
os.system('say "data is loaded"')
# Consider trying different values for output_layers
print('\nstarting nn on twitter with logit @ -2....')
tf = OverfeatTransformer(output_layers=[-2])
clf = LogisticRegression()
# clf = SVC()
# clf = RandomForestClassifier()
pipe = make_pipeline(tf, clf)
start_time = time.time()
print(__doc__)
from sklearn_theano.datasets import fetch_asirra
from sklearn_theano.feature_extraction import OverfeatTransformer
from sklearn_theano.utils import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report, accuracy_score
import matplotlib.pyplot as plt
import time
asirra = fetch_asirra(image_count=20)
X = asirra.images.astype('float32')
y = asirra.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=.6,
random_state=1999)
tf = OverfeatTransformer(output_layers=[-3])
clf = LogisticRegression()
pipe = make_pipeline(tf, clf)
t0 = time.time()
pipe.fit(X_train, y_train)
print("Total transform time")
print("====================")
print(time.time() - t0)
print()
y_pred = pipe.predict(X_test)
print(classification_report(y_test, y_pred))
print()
print("Accuracy score")
print("==============")
"""
print(__doc__)
from sklearn_theano.datasets import fetch_asirra
from sklearn_theano.feature_extraction import OverfeatTransformer
from sklearn_theano.utils import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report, accuracy_score
import matplotlib.pyplot as plt
import time
asirra = fetch_asirra(image_count=20)
X = asirra.images.astype('float32')
y = asirra.target
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=.6, random_state=1999)
tf = OverfeatTransformer(output_layers=[-3])
clf = LogisticRegression()
pipe = make_pipeline(tf, clf)
t0 = time.time()
pipe.fit(X_train, y_train)
print("Total transform time")
print("====================")
print(time.time() - t0)
print()
y_pred = pipe.predict(X_test)
print(classification_report(y_test, y_pred))
print()
print("Accuracy score")
print("==============")
print(accuracy_score(y_test, y_pred))
