In this case, the images come from the Asirra dataset functionality built into sklearn-theano. Plots show one example of each class (cats and dogs). """ 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()
from sklearn_theano.datasets import fetch_asirra from sklearn_theano.feature_extraction import OverfeatTransformer import matplotlib.pyplot as plt import time asirra = fetch_asirra() X = asirra.images.astype("float32") X = X[0:5] y = asirra.target all_times = [] for i in range(0, 15): tf = OverfeatTransformer(output_layers=[i]) t0 = time.time() X_tf = tf.transform(X) print("Shape of layer %i output" % i) print(X_tf.shape) t_o = time.time() - t0 all_times.append(t_o) print("Time for layer %i" % i, t_o) print() plt.plot(all_times, marker="o") plt.title("Runtime for input to layer X") plt.xlabel("Layer number") plt.ylabel("Time (seconds)") plt.show()
In this case, the images come from the Asirra dataset functionality built into sklearn-theano. Plots show one example of each class (cats and dogs). """ print(__doc__) from sklearn_theano.datasets import fetch_asirra from sklearn_theano.feature_extraction import OverfeatTransformer from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import train_test_split 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))
from sklearn_theano.datasets import fetch_asirra from sklearn_theano.feature_extraction import OverfeatTransformer import matplotlib.pyplot as plt import time asirra = fetch_asirra() X = asirra.images.astype('float32') X = X[0:5] y = asirra.target all_times = [] for i in range(0, 15): tf = OverfeatTransformer(output_layers=[i]) t0 = time.time() X_tf = tf.transform(X) print("Shape of layer %i output" % i) print(X_tf.shape) t_o = time.time() - t0 all_times.append(t_o) print("Time for layer %i" % i, t_o) print() plt.plot(all_times, marker='o') plt.title("Runtime for input to layer X") plt.xlabel("Layer number") plt.ylabel("Time (seconds)") plt.show()