【发布时间】：2017-08-18 16:58:12
【问题描述】：
我已经创建并训练了一个神经网络，但我希望能够输入测试点并查看其结果（而不是使用 eval 函数）。
模型运行良好，每个时期的成本都降低了，但我只想在最后添加一条线来传递一些输入坐标并让它告诉我预测的变换坐标。
import tensorflow as tf
import numpy as np
def coordinate_transform(size, angle):
input = np.random.rand(size, 2)
output = np.zeros((size, 2))
noise = 0.05*(np.add(np.random.rand(size) * 2, -1))
theta = np.add(np.add(np.arctan(input[:,1] / input[:,0]) , angle) , noise)
radii = np.sqrt(np.square(input[:,0]) + np.square(input[:,1]))
output[:,0] = np.multiply(radii, np.cos(theta))
output[:,1] = np.multiply(radii, np.sin(theta))
return input, output
#Data
input, output = coordinate_transform(2000, np.pi/2)
train_in = input[:1000]
train_out = output[:1000]
test_in = input[1000:]
test_out = output[1000:]
# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 1
display_step = 1
# Network Parameters
n_hidden_1 = 100 # 1st layer number of features
n_input = 2 # [x,y]
n_classes = 2 # output x,y coords
# tf Graph input
x = tf.placeholder("float", [1,n_input])
y = tf.placeholder("float", [1, n_input])
# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Output layer with linear activation
out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
return out_layer
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
#cost = tf.losses.mean_squared_error(0, (tf.slice(pred, 0, 1) - x)**2 + (tf.slice(pred, 1, 1) - y)**2)
cost = tf.losses.mean_squared_error(y, pred)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
optimizer = optimizer.minimize(cost)
# Initializing the variables
#init = tf.global_variables_initializer()
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = 1000#int(len(train_in)/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_x = train_in[i].reshape((1,2))
batch_y = train_out[i].reshape((1,2))
#print(batch_x.shape)
#print(batch_y.shape)
#print(batch_y, batch_x)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
y: batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#Make predictions
【问题讨论】：
标签： python machine-learning tensorflow neural-network