np.linspace np.random.randn
#ipython qtconsole --pylab=inline import numpy as np import matplotlib.pyplot as plt import tensorflow.keras as kr x = np.linspace(1,100,30) y = x*3+7+np.random.randn(30)*6 model = kr.Sequential() model.add(kr.layers.Dense(1, input_shape=(1,))) model.summary() model.compile(optimizer='adam', loss='mse') history = model.fit(x, y, epochs=5000) prediction = model.predict(x) plt.scatter(x,y) plt.scatter(x,prediction) plt.scatter(x,x*3+7) (plt.scatter(x,prediction),plt.scatter(x,y)) (plt.scatter(x,x*3+7), plt.scatter(x,prediction))
tf.keras.datasets.mnist
import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test, verbose=2) prediction = model.predict(x_test) for i in range(5): plt.ylabel('No.' + str(i)) plt.xlabel('actual: ' + str(y_test[i])) plt.title('prediction: ' + str(np.argmax(prediction[i]))) plt.imshow(x_test[i]) plt.show()
tf.keras.datasets.fashion_mnist
import tensorflow.keras as kr import matplotlib.pyplot as plt import numpy as np data = kr.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = data.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] train_images = train_images/255.0 test_images = test_images/255.0 model = kr.Sequential([ kr.layers.Flatten(input_shape=(28,28)), kr.layers.Dense(128, activation='relu'), kr.layers.Dense(10, activation='softmax')]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) test_loss, test_acc = model.evaluate(test_images, test_labels) prediction = model.predict(test_images) for i in range(5): plt.ylabel('No.' + str(i)) plt.xlabel('Actual: ' + class_names[test_labels[i]]) plt.title('prediction: ' + class_names[np.argmax(prediction[i])]) plt.imshow(test_images[i]) plt.show()