Keras is not learning anything - tensorflow

Question

I am trying to learn keras and none of the code I use is learning. From the example code on Deep Learning with Python to the code on https://medium.com/nybles/create-your-first-image-recognition-classifier-using-cnn-keras-and-tensorflow-backend-6eaab98d14dd. With the last link, I am not able to use the full 10000 dataset, but even with my 1589 size training dataset the accuracy still stays at .5 the whole time.
I'm almost starting to think the problem is my overclocked cpu, and ram, but thats more of a crazy guess.
I initially thought the problem was that I had the tensorflow2.0.0-alpha. However, even after I went to regular tensorflow-gpu still nothing is learning.
#Convolutional Neural Network
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.models import model_from_json
import os
#initialize the cnn
classifier = Sequential()
#Step 1 convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))
#Step 2 Pooling
classifier.add(MaxPooling2D(pool_size = (2,2)))
#Step 3 Flattening
classifier.add(Flatten())
#Step 4 Full Connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 64, activation = 'relu'))
classifier.add(Dense(output_dim = 32, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))
#Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
#Part 2 Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1/.255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory(
'dataset/training_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
from IPython.display import display
from PIL import Image
classifier.fit_generator(
training_set,
steps_per_epoch=1589,
epochs=10,
validation_data=test_set,
validation_steps=378)
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('dataset/test_set/cats/cat.4012.jpg', target_size = (64,64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] >= 0.5:
prediction = 'dog'
else:
prediction = 'cat'
print(prediction)
examples from deep-learning with python
from keras.datasets import imdb
(train_data, train_labels), (test_data, test_labels) =
imdb.load_data(num_words = 10000)
import numpy as np
def vectorize_sequences(sequences, dimension=10000):
results = np.zeros((len(sequences), dimension))
for i,sequence in enumerate(sequences):
results[i, sequence]=1.
return results
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(16, activation='relu',input_shape=(10000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(partial_x_train,
partial_y_train,
epochs=20,
batch_size=512,
validation_data=(x_val,y_val))
dogs an cats output:
Epoch 1/10
1589/1589 [==============================] - 112s 70ms/step - loss: 7.8736 - acc: 0.5115 - val_loss: 7.9528 - val_acc: 0.4976
Epoch 2/10
1589/1589 [==============================] - 111s 70ms/step - loss: 7.8697 - acc: 0.5117 - val_loss: 7.9606 - val_acc: 0.4971
Epoch 3/10
1589/1589 [==============================] - 111s 70ms/step - loss: 7.8740 - acc: 0.5115 - val_loss: 7.9499 - val_acc: 0.4978
Epoch 4/10
1589/1589 [==============================] - 111s 70ms/step - loss: 7.8674 - acc: 0.5119 - val_loss: 7.9634 - val_acc: 0.4969
Epoch 5/10
1589/1589 [==============================] - 111s 70ms/step - loss: 7.8765 - acc: 0.5113 - val_loss: 7.9499 - val_acc: 0.4977
Epoch 6/10
1589/1589 [==============================] - 111s 70ms/step - loss: 7.8737 - acc: 0.5115 - val_loss: 7.9634 - val_acc: 0.4970
Epoch 7/10
1589/1589 [==============================] - 129s 81ms/step - loss: 7.8623 - acc: 0.5122 - val_loss: 7.9626 - val_acc: 0.4970
Epoch 8/10
1589/1589 [==============================] - 112s 71ms/step - loss: 7.8758 - acc: 0.5114 - val_loss: 7.9508 - val_acc: 0.4977
Epoch 9/10
1589/1589 [==============================] - 115s 72ms/step - loss: 7.8708 - acc: 0.5117 - val_loss: 7.9519 - val_acc: 0.4976
Epoch 10/10
1589/1589 [==============================] - 112s 70ms/step - loss: 7.8738 - acc: 0.5115 - val_loss: 7.9614 - val_acc: 0.4971
cat
deeplearning imdb example output:
WARNING:tensorflow:From C:\Users\Mike\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 15000 samples, validate on 10000 samples
Epoch 1/20
15000/15000 [==============================] - 4s 246us/step - loss: 0.6932 - acc: 0.4982 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 2/20
15000/15000 [==============================] - 2s 115us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 3/20
15000/15000 [==============================] - 2s 115us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 4/20
15000/15000 [==============================] - 2s 119us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 5/20
15000/15000 [==============================] - 2s 120us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 6/20
15000/15000 [==============================] - 2s 119us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 7/20
15000/15000 [==============================] - 2s 113us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 8/20
15000/15000 [==============================] - 2s 113us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 9/20
15000/15000 [==============================] - 2s 119us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 10/20
15000/15000 [==============================] - 2s 122us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 11/20
15000/15000 [==============================] - 2s 116us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 12/20
15000/15000 [==============================] - 2s 116us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 13/20
15000/15000 [==============================] - 2s 121us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6933 - val_acc: 0.4947
Epoch 14/20
15000/15000 [==============================] - 2s 127us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 15/20
15000/15000 [==============================] - 2s 121us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 16/20
15000/15000 [==============================] - 2s 113us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 17/20
15000/15000 [==============================] - 2s 115us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 18/20
15000/15000 [==============================] - 2s 114us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 19/20
15000/15000 [==============================] - 2s 114us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947
Epoch 20/20
15000/15000 [==============================] - 2s 119us/step - loss: 0.6931 - acc: 0.5035 - val_loss: 0.6932 - val_acc: 0.4947