b_fc2 = tf.Variable(mo.idx_to_tensor(dir + "b_fc2"))
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
#### Add a way to set the number of loops
#### Also create a way to safely interrrupt the process
## Also, this is going to lead to overfitting, just to let you know
if len(sys.argv) > 1 and sys.argv[1] == "placeholders":
dir = "weights/"
batch = mnist.train.next_batch(50)
mo.tensor_to_idx(x_image.eval(feed_dict={x: [batch[0][0]]}), dir + "x_image")
mo.tensor_to_idx(h_conv1.eval(feed_dict={x: [batch[0][0]]}), dir + "h_conv1")
mo.tensor_to_idx(h_pool1.eval(feed_dict={x: [batch[0][0]]}), dir + "h_pool1")
mo.tensor_to_idx(h_conv2.eval(feed_dict={x: [batch[0][0]]}), dir + "h_conv2")
mo.tensor_to_idx(h_pool2.eval(feed_dict={x: [batch[0][0]]}), dir + "h_pool2")
mo.tensor_to_idx(h_fc1.eval(feed_dict={x: [batch[0][0]]}), dir + "h_fc1")
mo.tensor_to_idx(
(tf.matmul(h_fc1_drop, W_fc2) + b_fc2).eval(feed_dict={x: [batch[0][0]], keep_prob: 1.0}), dir + "y_conv"
)
print("x_image\t", tf.reduce_max(x_image).eval(feed_dict={x: [batch[0][0]]}))
print("h_conv1\t", tf.reduce_max(h_conv1).eval(feed_dict={x: [batch[0][0]]}))
print("h_pool1\t", tf.reduce_max(h_pool1).eval(feed_dict={x: [batch[0][0]]}))
print("h_conv2\t", tf.reduce_max(h_conv2).eval(feed_dict={x: [batch[0][0]]}))
print("h_pool2\t", tf.reduce_max(h_pool2).eval(feed_dict={x: [batch[0][0]]}))
print("h_fc1\t", tf.reduce_max(h_fc1).eval(feed_dict={x: [batch[0][0]]}))
print(
def write_weights():
dir = "weights/"
mo.tensor_to_idx(W_fc1, dir + "W_fc1")
mo.tensor_to_idx(b_fc1, dir + "b_fc1")
mo.tensor_to_idx(W_fc2, dir + "W_fc2")
mo.tensor_to_idx(b_fc2, dir + "b_fc2")
mo.tensor_to_idx(W_conv1, dir + "W_conv1")
mo.tensor_to_idx(b_conv1, dir + "b_conv1")
mo.tensor_to_idx(W_conv2, dir + "W_conv2")
mo.tensor_to_idx(b_conv2, dir + "b_conv2")
"""
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
'''
'''
mo.tensor_to_idx(W_fc1, dir+"W_fc1")
mo.tensor_to_idx(b_fc1, dir+"b_fc1")
mo.tensor_to_idx(W_fc2, dir+"W_fc2")
mo.tensor_to_idx(b_fc2, dir+"b_fc2")
mo.tensor_to_idx(W_conv1, dir+"W_conv1")
mo.tensor_to_idx(b_conv1, dir+"b_conv1")
mo.tensor_to_idx(W_conv2, dir+"W_conv2")
mo.tensor_to_idx(b_conv2, dir+"b_conv2")
'''
