I tried to work with sentdex tutorial "Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats" and got the error: "TypeError: float() argument must be a string or a number, not 'NoneType'". I don't know why, because I just changed the location where I import the Images from the harddrive to the google drive cloud. My Code:
from google.colab import drive
drive.mount('/content/drive')
import cv2
import numpy as np
import os
from random import shuffle
from tqdm import tqdm
TRAIN_DIR = '/content/drive/MyDrive/Facharbeit/train'
TEST_DIR = '/content/drive/MyDrive/Facharbeit/test'
IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic')
def label_img(img):
word_label = str(img.split('.')[-3])
if word_label == 'c': return [1,0]
elif word_label == 'd':return [0,1]
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
training_data.append([np.array(img),np.array(label)])
shuffle(training_data)
np.save('train_data.npy', training_data)
return training_data
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR,img)
img_num = img.split('.')[0]
img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
testing_data.append([np.array(img), img_num])
shuffle(testing_data)
np.save('test_data.npy', testing_data)
return testing_data
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
if os.path.exists('{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
train = train_data[:-500]
test = train_data[-500:]
model.fit({'input': X}, {'targets': Y}, n_epoch=3, validation_set=({'input': test_x}, {'targets': test_y}), snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
When running the code I got the error message:
Run id: dogsvscats-0.001-2conv-basic.model
Log directory: log/
---------------------------------
Training samples: 73500
Validation samples: 1500
--
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-33-489069081a3c> in <module>()
1 model.fit({'input': X}, {'targets': Y}, n_epoch=3, validation_set=({'input': test_x}, {'targets': test_y}),
----> 2 snapshot_step='500', show_metric=True, run_id=MODEL_NAME)
5 frames
/usr/local/lib/python3.6/dist-packages/tflearn/models/dnn.py in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, validation_batch_size, run_id, callbacks)
204 excl_trainops=excl_trainops,
205 run_id=run_id,
--> 206 callbacks=callbacks)
207
208 def retrieve_data_preprocessing_and_augmentation(self):
/usr/local/lib/python3.6/dist-packages/tflearn/helpers/trainer.py in fit(self, feed_dicts, n_epoch, val_feed_dicts, show_metric, snapshot_step, snapshot_epoch, shuffle_all, dprep_dict, daug_dict, excl_trainops, run_id, callbacks)
342 (bool(self.best_checkpoint_path) | snapshot_epoch),
343 snapshot_step,
--> 344 show_metric)
345
346 # Update training state
/usr/local/lib/python3.6/dist-packages/tflearn/helpers/trainer.py in _train(self, training_step, snapshot_epoch, snapshot_step, show_metric)
826 tflearn.is_training(True, session=self.session)
827 _, train_summ_str = self.session.run([self.train, self.summ_op],
--> 828 feed_batch)
829
830 # Retrieve loss value from summary string
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
966 try:
967 result = self._run(None, fetches, feed_dict, options_ptr,
--> 968 run_metadata_ptr)
969 if run_metadata:
970 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1158 feed_handles[subfeed_t.ref()] = subfeed_val
1159 else:
-> 1160 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
1161
1162 if (not is_tensor_handle_feed and
/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
TypeError: float() argument must be a string or a number, not 'NoneType'
Could somebody help me, I really don't know the mistake I made? I would be very thankful!