start_time = time.time() image = np.random.rand(64, 64) model = Linear() model.add(Conv2D(64, input_shape=(64, 64))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128)) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(128)) model.add(Dense(64)) model.add(Dense(1, activation=Sigmoid, normalize_signal=False)) model.summary() model.eval(image) input_data = open("input_data.pickle", "rb") input_data = np.array(pickle.load(input_data)) / 255.0 label = open("label.pickle", "rb") label = np.expand_dims(np.array(pickle.load(label)), axis=1) model.compile(optimizer=gradient_descent, loss=binary_crossentropy) model.fit(input_data, label, epochs=20, batch_size=16) end_time = time.time() print(f"Total execution time:: {end_time - start_time}") ''' model2 = Linear() model2.add(Dense(64, input_shape=(50,))) model2.add(Dense(32)) model2.add(Dense(1,activation=Sigmoid))
import time from layers import Dense, Flatten, Conv2D from model import Linear import numpy as np start_time = time.time() image = np.random.rand(64, 64) model = Linear() model.add(Conv2D(64)) model.add(Flatten()) model.add(Dense(512)) model.add(Dense(256)) model.add(Dense(64)) model.summary() print(model.eval(image)) end_time = time.time() print(f"Total execution time:: {end_time - start_time}")