詳解 ディープラーニング片手に4.3.1.3のプログラムを以下のようなプログラムで試しています。
import numpy as np
import tensorflow as tf
import chainer as Variable
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn.datasets import fetch_mldata
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
mnist=fetch_mldata('MNIST original', data_home=".")
np.random.seed(0)
n=len(mnist.data)
N=10000 #MNISTの一部データで実験
indices=np.random.permutation(range(n))[:N] #ランダムにN枚を選択
X=mnist.data[indices]
y=mnist.target[indices]
Y=np.eye(10)[y.astype(int)] #1-of-k表現に変換
X_train, X_test, Y_train, Y_test=train_test_split(X,Y,train_size=0.8)
n_in=len(X[0])#784
n_hidden=200
n_out=len(Y[0])#10
def prelu(X, alpha):
return tf.maximum(tf.zeros(tf.shape(X)), X)\
+alpha*tf.minimum(tf.zeros(tf.shape(X), X))
入力層＾隠れ層
w0=tf.Variable(tf.truncated_normal([n_in, n_hidden], stddev=0.01))
b0=tf.Variable(tf.zeros([n_hidden]))
alpha0=tf.Variable(tf.zeros([n_hidden]))
h0=prelu(tf.matmul(X, w0)+b0, alpha0)
隠れ層ー隠れ層
w1=tf.Variable(tf.truncated_normal([n_hidden, n_hidden], stddev=0.01))
b1=tf.Variable(tf.zeros([n_hidden]))
alpha1=tf.Variable(tf.zeros([n_hidden]))
h1=prelu(tf.matmul(h0, w1)+b1, alpha1)
w2=tf.Variable(tf.truncated_normal([n_hidden, n_hidden], stddev=0.01))
b2=tf.Variable(tf.zeros([n_hidden]))
alpha2=tf.Variable(tf.zeros([n_hidden]))
h2=prelu(tf.matmul(h1, w2)+b2, alpha2)
w3=tf.Variable(tf.truncated_normal([n_hidden, n_hidden], stddev=0.01))
b3=tf.Variable(tf.zeros([n_hidden]))
alpha3=tf.Variable(tf.zeros([n_hidden]))
h3=prelu(tf.matmul(h2, w3)+b3, alpha3)
隠れ層ー出力層
w4=tf.Variable(tf.truncated_normal([n_hidden, n_out], stddev=0.01))
b4=tf.Variable(tf.zeros([n_out]))
y=prelu(tf.matmul(h3, w4)+b4)
from keras.layers.advanced_activations import PReLU
model=Sequential()
model.add(Dense(n_hidden, imput_dim=n_in))
model.add(PReLU())
model.add(Dense(n_hidden))
model.add(PReLU())
model.add(Dense(n_hidden))
model.add(PReLU())
model.add(Dense(n_hidden))
model.add(PReLU())
model.add(Dense(n_out))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=0.01),
metrics=['accuracy'])
epochs=1000
batch_size=100
model.fit(X_train, Y_train,epochs=epochs,batch_size=batch_size)
loss_and_metrics=model.evaluate(X_test,Y_test)
print(loss_and_metrics)
この時エラーが以下のように表示されます。
TypeError Traceback (most recent call last)
in ()
37 b0=tf.Variable(tf.zeros([n_hidden]))
38 alpha0=tf.Variable(tf.zeros([n_hidden]))
---> 39 h0=prelu(tf.matmul(X, w0)+b0, alpha0)
40
41 #隠れ層ー隠れ層
~\Anaconda3\envs\TK1\lib\site-packages\tensorflow\python\ops\math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
1814 else:
1815 return gen_math_ops._mat_mul(
-> 1816 a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
1817
1818
~\Anaconda3\envs\TK1\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in _mat_mul(a, b, transpose_a, transpose_b, name)
1215 """
1216 result = _op_def_lib.apply_op("MatMul", a=a, b=b, transpose_a=transpose_a,
-> 1217 transpose_b=transpose_b, name=name)
1218 return result
1219
~\Anaconda3\envs\TK1\lib\site-packages\tensorflow\python\framework\op_def_library.py in apply_op(self, op_type_name, name, **keywords)
587 _SatisfiesTypeConstraint(base_type,
588 _Attr(op_def, input_arg.type_attr),
--> 589 param_name=input_name)
590 attrs[input_arg.type_attr] = attr_value
591 inferred_from[input_arg.type_attr] = input_name
~\Anaconda3\envs\TK1\lib\site-packages\tensorflow\python\framework\op_def_library.py in _SatisfiesTypeConstraint(dtype, attr_def, param_name)
58 "allowed values: %s" %
59 (param_name, dtypes.as_dtype(dtype).name,
---> 60 ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
61
62
TypeError: Value passed to parameter 'a' has DataType uint8 not in list of allowed values: float16, float32, float64, int32, complex64, complex128
これらを見てそれぞれどのように改善するべきなのか分からず途方に暮れています・・・