I try to learn tensorflow, and I wrote my first model. When I try to run this model tensorflow gives
TypeError: TF_SessionRun_wrapper: expected all values in input dict to be ndarray.
I check type of data in input dict, and they are ndarrays. Probably I wrongly preprocess data, that I feed to model.
import tensorflow as tf
import numpy as np
from sklearn.datasets import load_iris
np.random.seed(0)
data, labels = load_iris(return_X_y=True)
num_elements = len(labels)
shuffled_indices = np.arange(len(labels))
np.random.shuffle(shuffled_indices)
shuffled_data = data[shuffled_indices]
shuffled_labels = labels[shuffled_indices]
one_hot_labels = np.zeros([num_elements, 3], dtype=int)
one_hot_labels[np.arange(num_elements), shuffled_labels] = 1
train_data = shuffled_data[0:105]
train_labels = one_hot_labels[0:105]
test_data = shuffled_data[105:]
test_labels = one_hot_labels[105:]
def linear_model(input):
my_weights = tf.get_variable(name="weights", shape=[4, 3])
my_bias = tf.get_variable(name="bias", shape=[3])
linear_layer = tf.matmul(input, my_weights)
linear_layer_out = tf.nn.bias_add(value=linear_layer, bias=my_bias)
return linear_layer_out
x = tf.placeholder(tf.float32, shape=[None, 4], name="data_in")
y = tf.placeholder(tf.int32, shape=[None, 3], name="target_labels")
model_out = linear_model(x)
initializer = tf.global_variables_initializer()
loss = tf.reduce_mean(tf.losses.hinge_loss(logits=model_out, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
correct_prediction = tf.equal(tf.argmax(model_out, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(initializer)
for i in range(1000):
batch_x, batch_y = train_data[:, :], train_labels[:, :]
loss_val, _ = sess.run([loss, optimizer], feed_dict={x: batch_x, y: batch_y})
