#Saving and restoring a model (50) import numpy as np import tensorflow as tf import chapitre_9_01 as ch ch.reset_graph() from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() m, n = housing.data.shape from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaled_housing_data = scaler.fit_transform(housing.data) scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data] n_epochs = 1000 # not shown in the book learning_rate = 0.01 # not shown X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name="X") # not shown y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name="y") # not shown theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta") y_pred = tf.matmul(X, theta, name="predictions") # not shown error = y_pred - y # not shown mse = tf.reduce_mean(tf.square(error), name="mse") # not shown optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) # not shown training_op = optimizer.minimize(mse) # not shown init = tf.global_variables_initializer() saver = tf.train.Saver()
import tensorflow as tf
import chapitre_9_01 as ch1
ch1.reset_graph()
x1 = tf.Variable(1)
print(x1.graph is tf.get_default_graph())
graph = tf.Graph()
with graph.as_default():
x2 = tf.Variable(2)
print(x2.graph is graph)
print(x2.graph is tf.get_default_graph())
w = tf.constant(3)
x = w + 2
y = x + 5
z = x * 3
with tf.Session() as sess:
print(y.eval()) # 10
print(z.eval()) # 15
with tf.Session() as sess:
y_val, z_val = sess.run([y, z])
print(y_val) # 10
print(z_val) # 15