Sure:
Keras
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

Metrics Keras:
Train on 36 samples, validate on 4 samples
Epoch 1/40
36/36 [==============================] - 11s 311ms/step - loss: 1.8301 - acc: 0.3889 - val_loss: 12.0227 - val_acc: 0.2500
Epoch 2/40
36/36 [==============================] - 6s 173ms/step - loss: 7.4835 - acc: 0.4722 - val_loss: 12.0227 - val_acc: 0.2500
Epoch 3/40
36/36 [==============================] - 6s 170ms/step - loss: 6.9669 - acc: 0.5278 - val_loss: 12.0227 - val_acc: 0.2500
Epoch 4/40
36/36 [==============================] - 6s 169ms/step - loss: 5.1954 - acc: 0.5278 - val_loss: 4.0076 - val_acc: 0.7500
Epoch 5/40
36/36 [==============================] - 7s 182ms/step - loss: 7.3551 - acc: 0.4722 - val_loss: 3.4672 - val_acc: 0.7500
Epoch 6/40
36/36 [==============================] - 7s 184ms/step - loss: 6.0102 - acc: 0.4722 - val_loss: 1.2699 - val_acc: 0.2500
Epoch 7/40
36/36 [==============================] - 6s 177ms/step - loss: 0.8226 - acc: 0.6944 - val_loss: 0.4165 - val_acc: 0.7500
Epoch 8/40
36/36 [==============================] - 6s 178ms/step - loss: 0.7371 - acc: 0.6389 - val_loss: 0.6003 - val_acc: 0.7500
Epoch 9/40
36/36 [==============================] - 6s 178ms/step - loss: 0.2003 - acc: 0.9444 - val_loss: 0.9687 - val_acc: 0.2500
Epoch 10/40
36/36 [==============================] - 7s 186ms/step - loss: 0.2384 - acc: 0.9167 - val_loss: 0.6743 - val_acc: 0.5000
Epoch 11/40
36/36 [==============================] - 6s 173ms/step - loss: 0.1137 - acc: 0.9722 - val_loss: 0.1711 - val_acc: 1.0000
Epoch 12/40
36/36 [==============================] - 6s 168ms/step - loss: 0.0440 - acc: 1.0000 - val_loss: 0.9161 - val_acc: 0.5000
Epoch 13/40
36/36 [==============================] - 6s 172ms/step - loss: 0.0463 - acc: 1.0000 - val_loss: 0.1495 - val_acc: 1.0000

TensorFlow:
nn = tf.layers.conv2d(X_train_placeholder, 32, kernel_size=(3, 3), activation='relu')
nn = tf.layers.conv2d(nn, 64, kernel_size=(3, 3), activation='relu')
nn = tf.layers.max_pooling2d(nn, pool_size=(2, 2), strides=2)
nn = tf.layers.dropout(nn, 0.25)
nn = tf.layers.flatten(nn)
nn = tf.layers.dense(nn, 128, activation='relu')
nn = tf.layers.dropout(nn, 0.5)
nn = tf.layers.dense(nn, 2)

Metrics TF:
Currently on step 0
Loss:  0.7080002
Training accuracy is:
0.5277778
Validation accuracy is:
0.25


Currently on step 2
Loss:  0.8956334
Training accuracy is:
0.4722222
Validation accuracy is:
0.75


Currently on step 4
Loss:  2.839597
Training accuracy is:
0.4722222
Validation accuracy is:
0.75


Currently on step 6
Loss:  0.9275551
Training accuracy is:
0.5555556
Validation accuracy is:
0.25


Currently on step 8
Loss:  0.81815374
Training accuracy is:
0.5277778
Validation accuracy is:
0.25


Currently on step 10
Loss:  0.97793007
Training accuracy is:
0.5277778
Validation accuracy is:
0.25


Currently on step 12
Loss:  0.80247444
Training accuracy is:
0.5277778
Validation accuracy is:
0.25


Currently on step 14
Loss:  0.600952
Training accuracy is:
0.9444444
Validation accuracy is:
1.0