ImportError: DLL load failed: %1 is not a valid Win32 application.

[LiTing]

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import tensorflow as tf import numpy as np import pickle, os, cv2   tf.logging.set_verbosity(tf.logging.INFO) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'   def get_image_size():     img = cv2.imread('gestures/0/100.jpg', 0)     return img.shape   def get_num_of_classes():     return len(os.listdir('gestures/'))   image_x, image_y = get_image_size()   def cnn_model_fn(features, labels, mode):     input_layer = tf.reshape(features["x"], [-1, image_x, image_y, 1], name="input")       conv1 = tf.layers.conv2d(       inputs=input_layer,       filters=16,       kernel_size=[2, 2],       padding="same",       activation=tf.nn.relu,       name="conv1")     print("conv1",conv1.shape)     pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2, name="pool1")     print("pool1",pool1.shape)       conv2 = tf.layers.conv2d(       inputs=pool1,       filters=32,       kernel_size=[5, 5],       padding="same",       activation=tf.nn.relu,       name="conv2")     print("conv2",conv2.shape)     pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[5, 5], strides=5, name="pool2")     print("pool2",pool2.shape)       conv3 = tf.layers.conv2d(       inputs=pool2,       filters=64,       kernel_size=[5, 5],       padding="same",       activation=tf.nn.relu,       name="conv3")     print("conv3",conv3.shape)       # Dense Layer     flat = tf.reshape(conv3, [-1, 5*5*64], name="flat")     print(flat.shape)     dense = tf.layers.dense(inputs=flat, units=128, activation=tf.nn.relu, name="dense")     print(dense.shape)     dropout = tf.layers.dropout(inputs=dense, rate=0.2, training=mode == tf.estimator.ModeKeys.TRAIN, name="dropout")       # Logits Layer     num_of_classes = get_num_of_classes()     logits = tf.layers.dense(inputs=dropout, units=num_of_classes, name="logits")       output_class = tf.argmax(input=logits, axis=1, name="output_class")     output_probab = tf.nn.softmax(logits, name="softmax_tensor")     predictions = {"classes": tf.argmax(input=logits, axis=1), "probabilities": tf.nn.softmax(logits, name="softmax_tensor")}     #tf.Print(tf.nn.softmax(logits, name="softmax_tensor"), [tf.nn.softmax(logits, name="softmax_tensor")])     if mode == tf.estimator.ModeKeys.PREDICT:         return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)       # Calculate Loss (for both TRAIN and EVAL modes)     onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=num_of_classes)     loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)       # Configure the Training Op (for TRAIN mode)     if mode == tf.estimator.ModeKeys.TRAIN:         optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-2)         train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())         return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)       # Add evaluation metrics (for EVAL mode)     eval_metric_ops = {"accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])}     return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)   def main(argv):     with open("train_images", "rb") as f:         train_images = np.array(pickle.load(f))     with open("train_labels", "rb") as f:         train_labels = np.array(pickle.load(f), dtype=np.int32)       with open("test_images", "rb") as f:         test_images = np.array(pickle.load(f))     with open("test_labels", "rb") as f:         test_labels = np.array(pickle.load(f), dtype=np.int32)     #print(len(train_images[1]), len(train_labels))       classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="tmp/cnn_model3")       tensors_to_log = {"probabilities": "softmax_tensor"}     logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)       train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_images}, y=train_labels, batch_size=500, num_epochs=10, shuffle=True)     classifier.train(input_fn=train_input_fn, hooks=[logging_hook])       # Evaluate the model and print results     eval_input_fn = tf.estimator.inputs.numpy_input_fn(       x={"x": test_images},       y=test_labels,       num_epochs=1,       shuffle=False)     test_results = classifier.evaluate(input_fn=eval_input_fn)     print(test_results)     if __name__ == "__main__":     tf.app.run()

My error is Traceback (most recent call last):

Error:
  File "C:\Users\NTU\Desktop\Sign-Language-master\cnn_tf.py", line 1, in <module>
    import tensorflow as tf
  File "C:\Users\NTU\Anaconda3\envs\dip\lib\site-packages\tensorflow\__init__.py", line 22, in <module>
    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\Users\NTU\Anaconda3\envs\dip\lib\site-packages\tensorflow\python\__init__.py", line 27, in <module>
    import ctypes
  File "C:\Users\NTU\Anaconda3\envs\dip\lib\ctypes\__init__.py", line 7, in <module>
    from _ctypes import Union, Structure, Array
ImportError: DLL load failed: %1 is not a valid Win32 application.this:

Anyone can help me

[snippsat]

ImportError: DLL load failed: %1 is not a valid Win32 application.this:

This error is always a mix up 32-bit - 64-bit.
If you start Anaconda.

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Python 3.6.5 |Anaconda custom (64-bit)| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>

[MSC v.1900 64 bit (AMD64)] Anacoda use here Python 64-bit.
If 32-bit [MSC v.1914 32 bit (Intel)].
TensorFlow requires a 64-bit OS.
All install trough conda or pip has to be 64-bit when use 64-bit Python.