= tf.convert_to_tensor(_Y)
out1 = tf.matmul(X, Y)TypeError: Valuepassed to parameter 'a' hasDataType int64 not in list of allowed values: float16, float32, float64,
as K return K.sum(K.less_equal(K.abs(y_true-y_pred), 8)) / y_true.shapeloss='mean_squared_error', optimizer='adam', metrics=['accuracy', inRange])TypeError: Valuepassed to parameter</e
TypeError: Valuepassed to parameter 'input' hasDataType uint8 not in list of allowed values: float16, bfloat16, float32, float64image = tf.image.decode_jpeg(img_tbp)
image = tf.cast
to_categorical(y_test)model.add(Conv2D(64, kernel_size=3, activation="relu", input_shapetf.keras.models.load_model("model")print(predictions[10]) 我得到了这个错误 TypeError: Valuepassed to par
kernel_initializer=utils.truncated_normal_complex(),然而,我得到了这个错误:
TypeError: Valuepassed to parameter 'input' hasDataType complex128 not in list of allowed values: float16, float32
def generate_population(self): weights_origin = model.get_weights()
shape_listof w -> save shape into shape_list -> generate binary_value that has the shape of shape_list#-&
.values.astype(int)
X = np.array(X).reshape(X.shape[0], X.shape[1], 1)
#split data into 80% training (X_train, y_train) and 20% testing (X_test, y_testValuepassed to parameter 'input'