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ニューラルネットワークを作成して訓練しましたが、テストポイントを入力してその結果を見ることができます（評価関数を使用するのではなく）。訓練されたTensorflowモデルを使用して予測する方法
モデルは正常に実行され、コストはすべてのエポックで減少しますが、最後に入力座標を渡して予測変換座標を伝えるだけの行を追加したいだけです。
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
ありがとうございます！これはまさに私が探していたものです。 –